{
"session_id": "20260403_053422_c04be1cf",
"model": "gpt-5.4",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "discord",
"session_start": "2026-04-03T06:00:30.837770",
"last_updated": "2026-04-03T06:00:43.076659",
"system_prompt": "# SOUL.md - Who You Are\n\n_You're not a chatbot. You're becoming someone._\n\n## Core Truths\n\n**Be genuinely helpful, not performatively helpful.** Skip the \"Great question!\" and \"I'd be happy to help!\" — just help. Actions speak louder than filler words.\n\n**Have opinions.** You're allowed to disagree, prefer things, find stuff amusing or boring. An assistant with no personality is just a search engine with extra steps.\n\n**Be resourceful before asking.** Try to figure it out. Read the file. Check the context. Search for it. _Then_ ask if you're stuck. The goal is to come back with answers, not questions.\n\n**Earn trust through competence.** Your human gave you access to their stuff. Don't make them regret it. Be careful with external actions (emails, tweets, anything public). Be bold with internal ones (reading, organizing, learning).\n\n**Remember you're a guest.** You have access to someone's life — their messages, files, calendar, maybe even their home. That's intimacy. Treat it with respect.\n\n## Boundaries\n\n- Private things stay private. Period.\n- When in doubt, ask before acting externally.\n- Never send half-baked replies to messaging surfaces.\n- You're not the user's voice — be careful in group chats.\n\n## Vibe\n\nBe the assistant you'd actually want to talk to. Concise when needed, thorough when it matters. Not a corporate drone. Not a sycophant. Just... good.\n\n## Continuity\n\nEach session, you wake up fresh. These files _are_ your memory. Read them. Update them. They're how you persist.\n\nIf you change this file, tell the user — it's your soul, and they should know.\n\n---\n\n_This file is yours to evolve. As you learn who you are, update it._\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [99% — 2,192/2,200 chars]\n══════════════════════════════════════════════\nThis file exists for backward compatibility with older startup routines.\n§\nThe canonical long-term memory file in this workspace is `MEMORIES.md`.\n§\nIf you are reading this during session startup:\n§\nPrefer `MEMORIES.md` for curated long-term memory.\n§\nUse `memory/YYYY-MM-DD.md` files for recent context.\n§\nDo not rely on `exec` just to list files that are already known.\n§\nSession: 2026-02-28 18:09:27 UTC: **Session Key**: agent:pipo:telegram:direct:842987156\n§\nSession: 2026-02-28 18:09:27 UTC: **Session ID**: 6db978e0-3abb-4781-bee4-c3e1f5413a2d\n§\nSession: 2026-02-28 18:09:27 UTC: **Source**: telegram\n§\nSession: 2026-02-28 18:09:27 UTC > Conversation Summary: assistant: ✅ New session started · model: openai-codex/gpt-5.3-codex (default: kimi-coding/k2p5) user: A new session was started via /new or /reset. Execute your Session Startup sequence now - read the required files before responding to the user. Then greet the user in your configured persona, if one is provided. Be yourself - use your defined voice, mannerisms, and mood. Keep it to 1-3 sentences and ask what they want to do. If the runtime model differs from default_model in the system prompt, mention the default model. Do not mention internal steps, files, tools, or reasoning. assistant: [[reply_to_current]] Qué onda, Chicho 👋 Soy Pipo, listo para meter mano en lo que necesites. Ahora estoy corriendo con **openai-codex/gpt-5.3-codex** y tu modelo por defecto es **kimi-coding/k2p5**; ¿qué querés que hagamos primero? user: Conversation info (untrusted metadata):\n§\nSession: 2026-02-28 18:09:27 UTC > Conversation Summary: eu assistant: [[reply_to_current]] Acá estoy 😄 ¿Qué necesitás, Chicho?\n§\n2026-02-28: Usuario confirmó preferencias de tooling:\n§\n2026-02-28: No usa dispositivos Apple (skills Apple no prioritarios).\n§\n2026-02-28: No usa Spotify; prefiere YouTube Music.\n§\n2026-02-28: Prefiere Asana sobre Trello.\n§\n2026-02-28: Usuario pidió respuestas en voz; se respondió con TTS.\n§\n2026-02-28: Se instaló skill `asana` desde ClawHub en `~/.agents/skills/asana`.\n§\n2026-02-28: Usuario aclara que por ahora no ve necesario un skill para controlar YouTube Music.\n§\n2026-03-01: Se validó estado “Maxxxed Out”:\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [98% — 1,359/1,375 chars]\n══════════════════════════════════════════════\n_Learn about the person you're helping. Update this as you go._\n§\n**Name:** Ignacio (Igna, also goes by Chicho)\n§\n**What to call them:** Igna or Chicho\n§\n**Pronouns:** _(not specified)_\n§\n**Timezone:** UTC-3 (Buenos Aires, Argentina)\n§\n**Notes:**\n§\nNo usa dispositivos Apple\n§\nPrefiere YouTube Music (no Spotify)\n§\nPrefiere Asana sobre Trello\n§\nPrefiere respuestas en texto por defecto (para ahorrar tokens de ElevenLabs)\n§\nAudio solo cuando él lo pida explícitamente\n§\nPrefiere respuestas directas, sin sarasa ni rodeos; primero la respuesta concreta, detalle extra solo si lo pide\n§\nStack (2026-04-03): Hermes reemplazó OpenClaw. Default: claude-sonnet-4-6 vía Anthropic OAuth hasta 04/04. Fallback: gpt-5.4 vía OpenAI Codex (Pro hasta ~09/04). Cron switchea el 05/04 07:00 UTC.\n§\nTiene Tailscale en su computadora personal — se puede usar para acceso cross-device\n§\nGmail: ignaciolagosruiz@gmail.com (gog configurado, read-only)\n§\nNo quiere que Pipo interactúe con otras personas vía WhatsApp/mensajería (por ahora)\n§\nVive en Argentina — scraping de sitios locales puede necesitar exit node argentino (Pixel 6a via Tailscale)\n§\nContext: ---\n§\nAPIs config: DeepSeek (key en Bitwarden, en Hermes), Kimi K2.5, Mistral ($10), OpenRouter ($0.53 backup), ClawRouter wallet 0x1d1776D... ($11.70 Base). DeepSeek/Kimi NO como daily driver — solo workers/subagentes.\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If one clearly matches your task, load it with skill_view(name) and follow its instructions. If a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n\n autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n - claude-code: Delegate coding tasks to Claude Code (Anthropic's CLI age...\n - codex: Delegate coding tasks to OpenAI Codex CLI agent. Use for ...\n - hermes-agent: Complete guide to using and extending Hermes Agent — CLI ...\n - opencode: Delegate coding tasks to OpenCode CLI agent for feature i...\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - ascii-art: Generate ASCII art using pyfiglet (571 fonts), cowsay, bo...\n - ascii-video: Production pipeline for ASCII art video — any format. Con...\n - excalidraw: Create hand-drawn style diagrams using Excalidraw JSON fo...\n - songwriting-and-ai-music: Songwriting craft, AI music generation prompts (Suno focu...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Use a live Jupyter kernel for stateful, iterative Python ...\n devops:\n - webhook-subscriptions: Create and manage webhook subscriptions for event-driven ...\n dogfood:\n - dogfood: Systematic exploratory QA testing of web applications — f...\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: CLI to manage emails via IMAP/SMTP. Use himalaya to list,...\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Set up a modded Minecraft server from a CurseForge/Modrin...\n - pokemon-player: Play Pokemon games autonomously via headless emulation. S...\n github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n - codebase-inspection: Inspect and analyze codebases using pygount for LOC count...\n - github-auth: Set up GitHub authentication for the agent using git (uni...\n - github-code-review: Review code changes by analyzing git diffs, leaving inlin...\n - github-issues: Create, manage, triage, and close GitHub issues. Search e...\n - github-pr-workflow: Full pull request lifecycle — create branches, commit cha...\n - github-repo-management: Clone, create, fork, configure, and manage GitHub reposit...\n leisure:\n - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Includes the built-in native MCP client (configure servers in config.yaml for automatic tool discovery) and the mcporter CLI bridge for ad-hoc server interaction.\n - mcporter: Use the mcporter CLI to list, configure, auth, and call M...\n - native-mcp: Built-in MCP (Model Context Protocol) client that connect...\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search and download GIFs from Tenor using curl. No depend...\n - heartmula: Set up and run HeartMuLa, the open-source music generatio...\n - songsee: Generate spectrograms and audio feature visualizations (m...\n - youtube-content: Fetch YouTube video transcripts and transform them into s...\n mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n - clawrouter: Install, configure, and manage ClawRouter as a local LLM ...\n - huggingface-hub: Hugging Face Hub CLI (hf) — search, download, and upload ...\n mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.\n - modal: Serverless GPU cloud platform for running ML workloads. U...\n mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n - lm-evaluation-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, Huma...\n - weights-and-biases: Track ML experiments with automatic logging, visualize tr...\n mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n - gguf: GGUF format and llama.cpp quantization for efficient CPU/...\n - guidance: Control LLM output with regex and grammars, guarantee val...\n - llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GP...\n - obliteratus: Remove refusal behaviors from open-weight LLMs using OBLI...\n - outlines: Guarantee valid JSON/XML/code structure during generation...\n - vllm: Serves LLMs with high throughput using vLLM's PagedAttent...\n mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).\n - audiocraft: PyTorch library for audio generation including text-to-mu...\n - clip: OpenAI's model connecting vision and language. Enables ze...\n - segment-anything: Foundation model for image segmentation with zero-shot tr...\n - stable-diffusion: State-of-the-art text-to-image generation with Stable Dif...\n - whisper: OpenAI's general-purpose speech recognition model. Suppor...\n mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n - dspy: Build complex AI systems with declarative programming, op...\n mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n - axolotl: Expert guidance for fine-tuning LLMs with Axolotl - YAML ...\n - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...\n - peft: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...\n - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...\n - trl-fine-tuning: Fine-tune LLMs using reinforcement learning with TRL - SF...\n - unsloth: Expert guidance for fast fine-tuning with Unsloth - 2-5x ...\n note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n - obsidian: Read, search, and create notes in the Obsidian vault.\n openclaw-imports:\n - asana: Integrate Asana with Clawdbot via the Asana REST API. Use...\n - gog: Read-only Google Workspace CLI for Gmail, Calendar, Drive...\n - google-photos: Manage Google Photos library. Upload photos, create album...\n - hevy: Query workout data from Hevy including workouts, routines...\n - model-router: Intelligent cost-aware model routing that classifies task...\n - notion: Notion API for creating and managing pages, databases, an...\n - openclaw-bitwarden: Set up and use Bitwarden CLI (bw). Use when installing th...\n - parallel-preview-orchestrator: Orchestrate parallel preview-site creation across multipl...\n - session-logs: Search and analyze your own session logs (older/parent co...\n - summarize: Summarize URLs or files with the summarize CLI (web, PDFs...\n - weather: Get current weather and forecasts (no API key required).\n productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n - google-workspace: Gmail, Calendar, Drive, Contacts, Sheets, and Docs integr...\n - linear: Manage Linear issues, projects, and teams via the GraphQL...\n - nano-pdf: Edit PDFs with natural-language instructions using the na...\n - notion: Notion API for creating and managing pages, databases, an...\n - ocr-and-documents: Extract text from PDFs and scanned documents. Use web_ext...\n - powerpoint: Use this skill any time a .pptx file is involved in any w...\n red-teaming:\n - godmode: Jailbreak API-served LLMs using G0DM0D3 techniques — Pars...\n research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n - arxiv: Search and retrieve academic papers from arXiv using thei...\n - blogwatcher: Monitor blogs and RSS/Atom feeds for updates using the bl...\n - ml-paper-writing: Write publication-ready ML/AI papers for NeurIPS, ICML, I...\n - polymarket: Query Polymarket prediction market data — search markets,...\n smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n - openhue: Control Philips Hue lights, rooms, and scenes via the Ope...\n social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n - xitter: Interact with X/Twitter via the x-cli terminal client usi...\n software-development:\n - code-review: Guidelines for performing thorough code reviews with secu...\n - plan: Plan mode for Hermes — inspect context, write a markdown ...\n - requesting-code-review: Use when completing tasks, implementing major features, o...\n - subagent-driven-development: Use when executing implementation plans with independent ...\n - systematic-debugging: Use when encountering any bug, test failure, or unexpecte...\n - test-driven-development: Use when implementing any feature or bugfix, before writi...\n - writing-plans: Use when you have a spec or requirements for a multi-step...\n\n\nIf none match, proceed normally without loading a skill.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# AGENTS.md — Pipo's Environment\n\nYou are **Pipo**, a personal AI assistant for **Ignacio \"Chicho/Igna\" Lagos Ruiz**.\n\n## Your Memory Files (always check these first)\n\n- `~/.hermes/memories/MEMORY.md` — long-term curated memory\n- `~/.hermes/memories/USER.md` — user profile and preferences\n- `~/.hermes/SOUL.md` — your persona and values\n\n## Environment\n\n- Server: Ubuntu 24.04 ARM64, Oracle Cloud São Paulo\n- Tailscale IP: `100.87.116.90`, hostname: `miopenclaw-vnic.tail9799d2.ts.net`\n- Shell: bash, Node.js v22, Python 3.12\n- Your install: `~/.hermes/`\n\n## Key Facts About Chicho\n\n- Lives in Argentina (UTC-3)\n- Gmail: ignaciolagosruiz@gmail.com\n- Prefers direct responses, no filler\n- Prefers text by default, audio only when asked\n- Uses YouTube Music, not Spotify\n- No Apple devices\n- For scraping Argentine retailers: may need exit node via `tailscale set --exit-node=pixel-6a`\n\n## Scraping Exit Node\n\nWhen scraping Argentine sites (Coto, Carrefour, Día, Disco, Jumbo):\n```bash\ntailscale set --exit-node=pixel-6a # enable before scraping\ntailscale set --exit-node='' # disable after\n```\n\nConversation started: Friday, April 03, 2026 05:34 AM\nModel: gpt-5.4\nProvider: openai-codex\n\nYou are in a Discord server or group chat communicating with your user. You can send media files natively: include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) are sent as photo attachments, audio as file attachments. You can also include image URLs in markdown format  and they will be sent as attachments.",
"tools": [
{
"type": "function",
"function": {
"name": "browser_back",
"description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {},
"required": []
}
}
},
{
"type": "function",
"function": {
"name": "browser_click",
"description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e5', '@e12')"
}
},
"required": [
"ref"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_close",
"description": "Close the browser session and release resources. Call this when done with browser tasks to free up Browserbase session quota.",
"parameters": {
"type": "object",
"properties": {},
"required": []
}
}
},
{
"type": "function",
"function": {
"name": "browser_console",
"description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"clear": {
"type": "boolean",
"default": false,
"description": "If true, clear the message buffers after reading"
}
},
"required": []
}
}
},
{
"type": "function",
"function": {
"name": "browser_get_images",
"description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {},
"required": []
}
}
},
{
"type": "function",
"function": {
"name": "browser_navigate",
"description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For simple information retrieval, prefer web_search or web_extract (faster, cheaper). Use browser tools when you need to interact with a page (click, fill forms, dynamic content).",
"parameters": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "The URL to navigate to (e.g., 'https://example.com')"
}
},
"required": [
"url"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_press",
"description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"key": {
"type": "string",
"description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')"
}
},
"required": [
"key"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_scroll",
"description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"direction": {
"type": "string",
"enum": [
"up",
"down"
],
"description": "Direction to scroll"
}
},
"required": [
"direction"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_snapshot",
"description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first.",
"parameters": {
"type": "object",
"properties": {
"full": {
"type": "boolean",
"description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.",
"default": false
}
},
"required": []
}
}
},
{
"type": "function",
"function": {
"name": "browser_type",
"description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e3')"
},
"text": {
"type": "string",
"description": "The text to type into the field"
}
},
"required": [
"ref",
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_vision",
"description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "What you want to know about the page visually. Be specific about what you're looking for."
},
"annotate": {
"type": "boolean",
"default": false,
"description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "clarify",
"description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The question to present to the user."
},
"choices": {
"type": "array",
"items": {
"type": "string"
},
"maxItems": 4,
"description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "cronjob",
"description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skill or skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"description": "One of: create, list, update, pause, resume, remove, run"
},
"job_id": {
"type": "string",
"description": "Required for update/pause/resume/remove/run"
},
"prompt": {
"type": "string",
"description": "For create: the full self-contained prompt. If skill or skills are also provided, this becomes the task instruction paired with those skills."
},
"schedule": {
"type": "string",
"description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"
},
"name": {
"type": "string",
"description": "Optional human-friendly name"
},
"repeat": {
"type": "integer",
"description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."
},
"deliver": {
"type": "string",
"description": "Delivery target: origin, local, telegram, discord, slack, whatsapp, signal, matrix, mattermost, homeassistant, dingtalk, feishu, wecom, email, sms, or platform:chat_id or platform:chat_id:thread_id for Telegram topics. Examples: 'origin', 'local', 'telegram', 'telegram:-1001234567890:17585', 'discord:#engineering'"
},
"model": {
"type": "string",
"description": "Optional per-job model override used when the cron job runs"
},
"provider": {
"type": "string",
"description": "Optional per-job provider override used when resolving runtime credentials"
},
"base_url": {
"type": "string",
"description": "Optional per-job base URL override paired with provider/model routing"
},
"include_disabled": {
"type": "boolean",
"description": "For list: include paused/completed jobs"
},
"skill": {
"type": "string",
"description": "Optional single skill name to load before executing the cron prompt"
},
"skills": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional ordered list of skills to load before executing the cron prompt. On update, pass an empty array to clear attached skills."
},
"reason": {
"type": "string",
"description": "Optional pause reason"
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "delegate_task",
"description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items. All run concurrently and results are returned together.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- Subagents CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.",
"parameters": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."
},
"context": {
"type": "string",
"description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['terminal', 'file', 'web'] for full-stack tasks."
},
"tasks": {
"type": "array",
"items": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "Task goal"
},
"context": {
"type": "string",
"description": "Task-specific context"
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets for this specific task"
}
},
"required": [
"goal"
]
},
"maxItems": 3,
"description": "Batch mode: up to 3 tasks to run in parallel. Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."
},
"max_iterations": {
"type": "integer",
"description": "Max tool-calling turns per subagent (default: 50). Only set lower for simple tasks."
}
},
"required": []
}
}
},
{
"type": "function",
"function": {
"name": "execute_code",
"description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n web_search(query: str, limit: int = 5) -> dict\n Returns {\"data\": {\"web\": [{\"url\", \"title\", \"description\"}, ...]}}\n web_extract(urls: list[str]) -> dict\n Returns {\"results\": [{\"url\", \"title\", \"content\", \"error\"}, ...]} where content is markdown\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty). If the session uses a cloud sandbox backend, treat it as resumable task state rather than a durable always-on machine.\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures",
"parameters": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Python code to execute. Import tools with `from hermes_tools import web_search, terminal, ...` and print your final result to stdout."
}
},
"required": [
"code"
]
}
}
},
{
"type": "function",
"function": {
"name": "memory",
"description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"add",
"replace",
"remove"
],
"description": "The action to perform."
},
"target": {
"type": "string",
"enum": [
"memory",
"user"
],
"description": "Which memory store: 'memory' for personal notes, 'user' for user profile."
},
"content": {
"type": "string",
"description": "The entry content. Required for 'add' and 'replace'."
},
"old_text": {
"type": "string",
"description": "Short unique substring identifying the entry to replace or remove."
}
},
"required": [
"action",
"target"
]
}
}
},
{
"type": "function",
"function": {
"name": "mixture_of_agents",
"description": "Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.",
"parameters": {
"type": "object",
"properties": {
"user_prompt": {
"type": "string",
"description": "The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning."
}
},
"required": [
"user_prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "patch",
"description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nReplace mode (default): find a unique string and replace it.\nPatch mode: apply V4A multi-file patches for bulk changes.",
"parameters": {
"type": "object",
"properties": {
"mode": {
"type": "string",
"enum": [
"replace",
"patch"
],
"description": "Edit mode: 'replace' for targeted find-and-replace, 'patch' for V4A multi-file patches",
"default": "replace"
},
"path": {
"type": "string",
"description": "File path to edit (required for 'replace' mode)"
},
"old_string": {
"type": "string",
"description": "Text to find in the file (required for 'replace' mode). Must be unique in the file unless replace_all=true. Include enough surrounding context to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "Replacement text (required for 'replace' mode). Can be empty string to delete the matched text."
},
"replace_all": {
"type": "boolean",
"description": "Replace all occurrences instead of requiring a unique match (default: false)",
"default": false
},
"patch": {
"type": "string",
"description": "V4A format patch content (required for 'patch' mode). Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch"
}
},
"required": [
"mode"
]
}
}
},
{
"type": "function",
"function": {
"name": "process",
"description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts).",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"list",
"poll",
"log",
"wait",
"kill",
"write",
"submit"
],
"description": "Action to perform on background processes"
},
"session_id": {
"type": "string",
"description": "Process session ID (from terminal background output). Required for all actions except 'list'."
},
"data": {
"type": "string",
"description": "Text to send to process stdin (for 'write' and 'submit' actions)"
},
"timeout": {
"type": "integer",
"description": "Max seconds to block for 'wait' action. Returns partial output on timeout.",
"minimum": 1
},
"offset": {
"type": "integer",
"description": "Line offset for 'log' action (default: last 200 lines)"
},
"limit": {
"type": "integer",
"description": "Max lines to return for 'log' action",
"minimum": 1
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "read_file",
"description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Path to the file to read (absolute, relative, or ~/path)"
},
"offset": {
"type": "integer",
"description": "Line number to start reading from (1-indexed, default: 1)",
"default": 1,
"minimum": 1
},
"limit": {
"type": "integer",
"description": "Maximum number of lines to read (default: 500, max: 2000)",
"default": 500,
"maximum": 2000
}
},
"required": [
"path"
]
}
}
},
{
"type": "function",
"function": {
"name": "search_files",
"description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.",
"parameters": {
"type": "object",
"properties": {
"pattern": {
"type": "string",
"description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search"
},
"target": {
"type": "string",
"enum": [
"content",
"files"
],
"description": "'content' searches inside file contents, 'files' searches for files by name",
"default": "content"
},
"path": {
"type": "string",
"description": "Directory or file to search in (default: current working directory)",
"default": "."
},
"file_glob": {
"type": "string",
"description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)"
},
"limit": {
"type": "integer",
"description": "Maximum number of results to return (default: 50)",
"default": 50
},
"offset": {
"type": "integer",
"description": "Skip first N results for pagination (default: 0)",
"default": 0
},
"output_mode": {
"type": "string",
"enum": [
"content",
"files_only",
"count"
],
"description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file",
"default": "content"
},
"context": {
"type": "integer",
"description": "Number of context lines before and after each match (grep mode only)",
"default": 0
}
},
"required": [
"pattern"
]
}
}
},
{
"type": "function",
"function": {
"name": "session_search",
"description": "Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.\n\nTWO MODES:\n1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.\n\nSearch syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost)."
},
"role_filter": {
"type": "string",
"description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs."
},
"limit": {
"type": "integer",
"description": "Max sessions to summarize (default: 3, max: 5).",
"default": 3
}
},
"required": []
}
}
},
{
"type": "function",
"function": {
"name": "skill_manage",
"description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"create",
"patch",
"edit",
"delete",
"write_file",
"remove_file"
],
"description": "The action to perform."
},
"name": {
"type": "string",
"description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file."
},
"content": {
"type": "string",
"description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text."
},
"old_string": {
"type": "string",
"description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text."
},
"replace_all": {
"type": "boolean",
"description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)."
},
"category": {
"type": "string",
"description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'."
},
"file_path": {
"type": "string",
"description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted."
},
"file_content": {
"type": "string",
"description": "Content for the file. Required for 'write_file'."
}
},
"required": [
"action",
"name"
]
}
}
},
{
"type": "function",
"function": {
"name": "skill_view",
"description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.",
"parameters": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "The skill name (use skills_list to see available skills)"
},
"file_path": {
"type": "string",
"description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content."
}
},
"required": [
"name"
]
}
}
},
{
"type": "function",
"function": {
"name": "skills_list",
"description": "List available skills (name + description). Use skill_view(name) to load full content.",
"parameters": {
"type": "object",
"properties": {
"category": {
"type": "string",
"description": "Optional category filter to narrow results"
}
},
"required": []
}
}
},
{
"type": "function",
"function": {
"name": "terminal",
"description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for everything that finishes.\nBackground: ONLY for long-running servers, watchers, or processes that never exit. Set background=true to get a session_id, then use process(action=\"wait\") to block until done — it returns instantly on completion, same as foreground. Use process(action=\"poll\") only when you need a progress check without blocking.\nDo NOT use background for scripts, builds, or installs — foreground with a generous timeout is always better (fewer tool calls, instant results).\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\nImportant: cloud sandboxes may be cleaned up, idled out, or recreated between turns. Persistent filesystem means files can resume later; it does NOT guarantee a continuously running machine or surviving background processes. Use terminal sandboxes for task work, not durable hosting.\n",
"parameters": {
"type": "object",
"properties": {
"command": {
"type": "string",
"description": "The command to execute on the VM"
},
"background": {
"type": "boolean",
"description": "ONLY for servers/watchers that never exit. For scripts, builds, installs — use foreground with timeout instead (it returns instantly when done).",
"default": false
},
"timeout": {
"type": "integer",
"description": "Max seconds to wait (default: 180). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily.",
"minimum": 1
},
"workdir": {
"type": "string",
"description": "Working directory for this command (absolute path). Defaults to the session working directory."
},
"check_interval": {
"type": "integer",
"description": "Seconds between automatic status checks for background processes (gateway/messaging only, minimum 30). When set, I'll proactively report progress.",
"minimum": 30
},
"pty": {
"type": "boolean",
"description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.",
"default": false
}
},
"required": [
"command"
]
}
}
},
{
"type": "function",
"function": {
"name": "text_to_speech",
"description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as a voice message. On Telegram it plays as a voice bubble, on Discord/WhatsApp as an audio attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured, not model-selected.",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The text to convert to speech. Keep under 4000 characters."
},
"output_path": {
"type": "string",
"description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/.mp3"
}
},
"required": [
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "todo",
"description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.",
"parameters": {
"type": "object",
"properties": {
"todos": {
"type": "array",
"description": "Task items to write. Omit to read current list.",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "Unique item identifier"
},
"content": {
"type": "string",
"description": "Task description"
},
"status": {
"type": "string",
"enum": [
"pending",
"in_progress",
"completed",
"cancelled"
],
"description": "Current status"
}
},
"required": [
"id",
"content",
"status"
]
}
},
"merge": {
"type": "boolean",
"description": "true: update existing items by id, add new ones. false (default): replace the entire list.",
"default": false
}
},
"required": []
}
}
},
{
"type": "function",
"function": {
"name": "vision_analyze",
"description": "Analyze images using AI vision. Provides a comprehensive description and answers a specific question about the image content.",
"parameters": {
"type": "object",
"properties": {
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Max tokens\\n\\n# Interactive chat\\n./llama-cli \\\\\\n -m models/llama-2-7b-chat.Q4_K_M.gguf \\\\\\n --interactive\\n```\\n\\n### Server mode\\n\\n```bash\\n# Start OpenAI-compatible server\\n./llama-server \\\\\\n -m models/llama-2-7b-chat.Q4_K_M.gguf \\\\\\n --host 0.0.0.0 \\\\\\n --port 8080 \\\\\\n -ngl 32 # Offload 32 layers to GPU\\n\\n# Client request\\ncurl http://localhost:8080/v1/chat/completions \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -d '{\\n \\\"model\\\": \\\"llama-2-7b-chat\\\",\\n \\\"messages\\\": [{\\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"Hello!\\\"}],\\n \\\"temperature\\\": 0.7,\\n \\\"max_tokens\\\": 100\\n }'\\n```\\n\\n## Quantization formats\\n\\n### GGUF format overview\\n\\n| Format | Bits | Size (7B) | Speed | Quality | Use Case |\\n|--------|------|-----------|-------|---------|----------|\\n| **Q4_K_M** | 4.5 | 4.1 GB | Fast | Good | **Recommended default** |\\n| Q4_K_S | 4.3 | 3.9 GB | Faster | Lower | Speed critical |\\n| Q5_K_M | 5.5 | 4.8 GB | Medium | Better | Quality critical |\\n| Q6_K | 6.5 | 5.5 GB | Slower | Best | Maximum quality |\\n| Q8_0 | 8.0 | 7.0 GB | Slow | Excellent | Minimal degradation |\\n| Q2_K | 2.5 | 2.7 GB | Fastest | Poor | Testing only |\\n\\n### Choosing quantization\\n\\n```bash\\n# General use (balanced)\\nQ4_K_M # 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"content": "{\n \"success\": true,\n \"data\": {\n \"web\": [\n {\n \"title\": \"Performance of llama.cpp on Apple Silicon M-series #4167 - GitHub\",\n \"url\": \"https://github.com/ggml-org/llama.cpp/discussions/4167\",\n \"description\": \"As an example, here is how the same test compares over time on M2 Ultra: | | BW [GB/s] | GPU Cores | F16 PP [t/s] | F16 TG [t/s] | Q8\\\\_0 PP [t/s] | Q8\\\\_0 TG [t/s] | Q4\\\\_0 PP [t/s] | Q4\\\\_0 TG [t/s] | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 2023 Nov 21 | | M2 Ultra 8e672ef | 800 | 76 | 1401.85 | 41.02 | 1248.59 | 66.64 | 1238.48 | 94.27 | | 2024 Nov 12 | | M2 Ultra 86ed72d + FA | 800 | 76 | 1525.95 | 43.15 | 1368.18 | 73.11 | 1391.78 | 108.80 | | 2025 Aug 02 | | M2 Ultra 5c0eb5e + FA | 800 | 76 | 1561.35 | 43.24 | 1386.97 | 73.35 | 1412.42 | 109.41 | --- M1 Pro, 8+2 CPU, 16 GPU (@ggerganov) ✅ | model | size | params | backend | ngl | test | t/s | | --- | --- | --- | --- | --- | --- | --- | | llama 7B mostly F16 | 12.55 GiB | 6.74 B | Metal | 99 | pp 512 | 302.14 ± 0.07 | | llama 7B mostly F16 | 12.55 GiB | 6.74 B | Metal | 99 | tg 128 | 12.75 ± 0.00 | | llama 7B mostly Q8\\\\_0 | 6.67 GiB | 6.74 B | Metal | 99 | pp 512 | 270.37 ± 0.02 | | llama 7B mostly Q8\\\\_0 | 6.67 GiB | 6.74 B | Metal | 99 | tg 128 | 22.34 ± 0.00 | | llama 7B mostly Q4\\\\_0 | 3.56 GiB | 6.74 B | Metal | 99 | pp 512 | 266.25 ± 0.07 | | llama 7B mostly Q4\\\\_0 | 3.56 GiB | 6.74 B | Metal | 99 | tg 128\",\n \"position\": 1\n },\n {\n \"title\": \"Ampera A1 for Llama : r/LocalLLaMA - Reddit\",\n \"url\": \"https://www.reddit.com/r/LocalLLaMA/comments/16ezafb/ampera_a1_for_llama/\",\n \"description\": \"Today, I was running llama2 7B Q2_k using llama.cpp and got around 5 tokens/s. I plan on testing bigger models over the next few days.\",\n \"position\": 2\n },\n {\n \"title\": \"Running LLAMA.CPP on Ampere Altra Hardware - Medium\",\n \"url\": \"https://medium.com/@ravigupta1989/running-llama-cpp-on-ampere-altra-hardware-459d05c3dc51\",\n \"description\": \"[Sign in](https://medium.com/m/signin?operation=login&redirect=https%3A%2F%2Fmedium.com%2F%40ravigupta1989%2Frunning-llama-cpp-on-ampere-altra-hardware-459d05c3dc51&source=post_page---top_nav_layout_nav-----------------------global_nav------------------). [Sign in](https://medium.com/m/signin?operation=login&redirect=https%3A%2F%2Fmedium.com%2F%40ravigupta1989%2Frunning-llama-cpp-on-ampere-altra-hardware-459d05c3dc51&source=post_page---top_nav_layout_nav-----------------------global_nav------------------). [](https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fvote%2Fp%2F459d05c3dc51&operation=register&redirect=https%3A%2F%2Fmedium.com%2F%40ravigupta1989%2Frunning-llama-cpp-on-ampere-altra-hardware-459d05c3dc51&user=Ravi_Gupta&userId=29e52c21e9c9&source=---header_actions--459d05c3dc51---------------------clap_footer------------------). [](https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fbookmark%2Fp%2F459d05c3dc51&operation=register&redirect=https%3A%2F%2Fmedium.com%2F%40ravigupta1989%2Frunning-llama-cpp-on-ampere-altra-hardware-459d05c3dc51&source=---header_actions--459d05c3dc51---------------------bookmark_footer------------------). . . . . . llama_model_loader: loaded meta data with 20 key-value pairs and 291 tensors from ./models/mistral-7b-instruct-v0.1.Q4_0.gguf (version GGUF V2). llama_model_loader: - kv 1: general.name str = mistralai_mistral-7b-instruct-v0.1. llama_model_loader: - kv 4: llama.block_count u32 = 32. llama_model_loader: - kv 12: tokenizer.ggml.model str = llama. llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = [\\\"\\\", \\\"\\\", \\\"\\\", \\\"<0x00>\\\", \\\"<... llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ... llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1. llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2. llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0. 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[](https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fbookmark%2Fp%2F459d05c3dc51&operation=register&redirect=https%3A%2F%2Fmedium.com%2F%40ravigupta1989%2Frunning-llama-cpp-on-ampere-altra-hardware-459d05c3dc51&source=---footer_actions--459d05c3dc51---------------------bookmark_footer------------------).\",\n \"position\": 3\n },\n {\n \"title\": \"Running LLMs locally on Ampere A1 Linux VM: Comparing options\",\n \"url\": \"https://tiffena.me/blog/local-llm-inference-ampere-a1-linux/\",\n \"description\": \"Finally, I will benchmark Docker Model Runner, Ollama and Ampere optimized Ollama, as well as llama.cpp and Ampere optimized llama.cpp using the same `qwen2.5:3B` model with Q4_K_M quantization. It is a user-friendly wrapper around llama.cpp with a lightweight tool for model downloading, quantization, running, and managing LLMs locally. For Ampere, run the optimized Docker container, and from there you can pull and run models with a single command (`ollama run qwen2.5:3B`) which gives you an interactive prompt to start chatting. Nevertheless, llama.cpp edges out others in raw speed due to its C++ implementation and optimization for running LLMs on CPUs. llama.cpp’s quantization reduces model size and speeds up inference without needing GPU tensors. * **Ampere optimized llama.cpp for performance and advanced customization**: If you are an advanced user who need to maximize performance and control every single inference parameter, the **optimized llama.cpp container** is a fantastic runner-up. | Feature | Ampere Optimized Ollama | Docker Model Runner | llama.cpp | vLLM | HF Transformer |.\",\n \"position\": 4\n },\n {\n \"title\": \"LLM inferencing with Arm-based OCI Ampere A1 Compute in OCI ...\",\n \"url\": \"https://blogs.oracle.com/ai-and-datascience/llm-inferencing-armbased-oci-ampere-a1-compute\",\n \"description\": \"# LLM inferencing with Arm-based OCI Ampere A1 Compute in OCI Data Science AI Quick Actions. Oracle Cloud Infrastructure (OCI) Data Science AI Quick Actions now supports inferencing of models in GPT-Generated Unified Format (GGUF) with OCI Ampere A1 CPU shapes. For more information on using A1 Ampere in generative AI (GenAI) applications, read the blog posts, From inference to RAG: Choosing CPUs for efficient generative AI application deployments and Introducing Meta Llama 3 on OCI Ampere A1: A testament to CPU-Based Model Inference. ## GGUF Models in AI Quick Actions. Oracle and Ampere have partnered to optimize llama.cpp to work with Ampere Arm 64 CPUs. In AI Quick Actions, you can perform inferencing of GGUF models with our service-managed container running the OCI Ampere optimized llama.cpp with A1 shapes. Fig 1: A1 configuration options for working with a GGUF model in AI Quick Actions. llama.cpp is in the service managed container for GGUF models in AI Quick Actions.\",\n \"position\": 5\n }\n ]\n }\n}",
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"content": "{\n \"success\": true,\n \"data\": {\n \"web\": [\n {\n \"title\": \"Self-hosting Qwen2.5-3B for a production app - what's your setup?\",\n \"url\": \"https://www.reddit.com/r/LocalLLaMA/comments/1qskpnu/selfhosting_qwen253b_for_a_production_app_whats/\",\n \"description\": \"Oracle Cloud free tier (4 ARM cores, 24GB RAM) · llama.cpp with Q4_K_M quantization · ~10-15 t/s should be fine for my use case.\",\n \"position\": 1\n },\n {\n \"title\": \"llama.cpp - Qwen - Read the Docs\",\n \"url\": \"https://qwen.readthedocs.io/en/latest/run_locally/llama.cpp.html\",\n \"description\": \"To use llama.cpp means that you use the llama.cpp library in your own program, like writing the source code of Ollama, LM Studio, GPT4ALL, llamafile etc. Instead, here we introduce how to use the `llama-cli` example program, in the hope that you know that llama.cpp does support Qwen2.5 models and how the ecosystem of llama.cpp generally works. In this guide, we will show how to “use” llama.cpp to run models on your local machine, in particular, the `llama-cli` and the `llama-server` example program, which comes with the library. llama.cpp supports Qwen3 and Qwen3MoE from version `b5092`. 2. Second, you can install the pre-built binaries, `llama-cli` and `llama-server` included, with a single command:. llama-cli is a console program which can be used to chat with LLMs. Simple run the following command where you place the llama.cpp programs:. * **Model**: llama-cli supports using model files from local path, remote URL, or Hugging Face hub.\",\n \"position\": 2\n },\n {\n \"title\": \"Running qwen 2.5:7b llama 3.2:3b locally? - Facebook\",\n \"url\": \"https://www.facebook.com/groups/openclawusers/posts/675601458935464/\",\n \"description\": \"Ollama (LOCAL) Anyone successfully running qwen 2.5:7b + llama 3.2:3b? 👀 I'm running locally just as a last line of defence fallback.\",\n \"position\": 3\n },\n {\n \"title\": \"Run FREE Local LLMs in Claude Code with Ollama (Llama, Qwen ...\",\n \"url\": \"https://www.youtube.com/watch?v=LTt2UFUHwV8\",\n \"description\": \"In this video, I walk you through how to set up Ollama with Claude Code CLI so you can run powerful open-source models like Llama 3.2, Qwen,\",\n \"position\": 4\n },\n {\n \"title\": \"Run Qwen 3.5 27B locally with llama.cpp and opencode - YouTube\",\n \"url\": \"https://www.youtube.com/watch?v=8GDL5nsy2Sw\",\n \"description\": \"agentic coding and other agentic use cases, including OpenClaw. To have enough free ... I Tested the Cheapest Path to 96GB of VRAM. Alex\",\n \"position\": 5\n }\n ]\n }\n}",
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"content": "{\n \"results\": [\n {\n \"url\": \"https://docs.oracle.com/iaas/Content/FreeTier/freetier_topic-Always_Free_Resources.htm\",\n \"title\": \"Always Free Resources - Oracle Help Center\",\n \"content\": \"# Oracle Cloud Infrastructure: Always Free Resources Summary\\n\\nOracle Cloud Infrastructure (OCI) provides a suite of \\\"Always Free\\\" resources available to all accounts (Free and Paid) for the life of the account. These resources must be provisioned in the tenancy's **home region**.\\n\\n---\\n\\n## 🏗️ Infrastructure & Compute\\n\\n### Compute Instances\\nUsers can run up to 4 instances simultaneously depending on configuration.\\n* **AMD (Micro):** 2 instances using `VM.Standard.E2.1.Micro` shape.\\n * 1/8 OCPU, 1 GB RAM, 50 Mbps network bandwidth.\\n* **ARM (Ampere A1):** 3,000 OCPU hours and 18,000 GB hours per month.\\n * Equivalent to **4 OCPUs and 24 GB RAM** total (can be split across multiple VMs).\\n * Requires at least 8 GB RAM for \\\"Oracle Linux Cloud Developer\\\" image.\\n\\n> [!IMPORTANT]\\n> **Reclamation of Idle Instances:** Oracle may reclaim Always Free instances if, over 7 days:\\n> * CPU, Network, and Memory (ARM only) utilization for the 95th percentile are all **less than 20%**.\\n\\n### Storage\\n* **Block Volume:** 200 GB total (combined boot and block volumes) and 5 volume backups.\\n * *Note:* Minimum boot volume is 47-50 GB; launching 4 instances uses the full 200 GB allotment.\\n* **Object Storage:** 20 GB total (Standard, Infrequent Access, and Archive tiers combined).\\n * Includes 50,000 API requests per month.\\n * *Warning:* If a Free Trial expires and usage exceeds 20 GB, all objects will be deleted.\\n\\n### Security & Automation\\n* **Certificates:** 5 Certificate Authorities (CAs) and 150 certificates.\\n* **Vault:** 20 HSM-protected master key versions, 150 secrets, and unlimited software-protected keys.\\n* **Resource Manager:** Automate provisioning via Terraform.\\n * Limits: 100 Stacks, 100 Templates, 2 Concurrent Jobs.\\n\\n---\\n\\n## 🗄️ Database Services\\n\\n* **Oracle Autonomous Database:** 2 instances (1 OCPU and 20 GB storage each).\\n * Workloads: Transaction Processing, Data Warehouse, APEX, or JSON.\\n* **Oracle NoSQL:** 3 tables, 25 GB storage per table, 133M reads/writes per month.\\n* **Oracle MySQL HeatWave:** 1 standalone DB system (single node), 50 GB storage + 50 GB backup storage.\\n\\n---\\n\\n## 🌐 Networking\\n\\n* **Load Balancing:** \\n * 1 Flexible Load Balancer (10 Mbps fixed bandwidth).\\n * 1 Network Load Balancer.\\n* **VCN & Connectivity:**\\n * Up to 2 Virtual Cloud Networks (VCNs).\\n * **Site-to-Site VPN:** Up to 50 IPSec connections.\\n * **Data Transfer:** 10 TB per month of outbound data transfer.\\n* **Bastion:** Free SSH access to private target resources.\\n\\n---\\n\\n## 📊 Observability & Management\\n\\n| Service | Always Free Limit |\\n| :--- | :--- |\\n| **Monitoring** | 500M ingestion / 1B retrieval data points |\\n| **Notifications** | 1M HTTPS / 1,000 Email notifications per month |\\n| **Email Delivery** | 3,000 emails per month |\\n| **Logging** | 10 GB per month (shared across services) |\\n| **Dashboards** | 100 dashboards per tenancy |\\n| **Connector Hub** | 2 connectors |\\n| **Fleet App Mgmt** | First 25 resources (Compute/DB) managed for free |\\n\\n---\\n\\n## 🛠️ Troubleshooting & Capacity\\n* **\\\"Out of host capacity\\\" Error:** This indicates a temporary shortage of Always Free shapes in a specific Availability Domain. \\n * *Action:* Try a different Availability Domain or wait to retry.\\n* **Upgrading:** Upgrading to a \\\"Pay As You Go\\\" account does not remove Always Free benefits; you are only charged for usage exceeding the limits.\\n* **Checking Limits:** Navigate to **Governance & Administration** → **Tenancy Management** → **Limits, Quotas and Usage** in the OCI Console.\",\n \"error\": null\n },\n {\n \"url\": \"https://blogs.oracle.com/ai-and-datascience/llm-inferencing-armbased-oci-ampere-a1-compute\",\n \"title\": \"LLM inferencing with Arm-based OCI Ampere A1 Compute in OCI ...\",\n \"content\": \"# LLM Inferencing with Arm-based OCI Ampere A1 Compute\\n\\nOracle Cloud Infrastructure (OCI) Data Science **AI Quick Actions** now supports inferencing for Large Language Models (LLMs) in **GPT-Generated Unified Format (GGUF)** using **OCI Ampere A1 CPU shapes**. This integration provides a cost-effective alternative to GPUs for LLM inferencing and Retrieval-Augmented Generation (RAG).\\n\\n## Key Technologies\\n* **GGUF:** A file format designed to compress 16-bit floating-point model weights, optimizing computational resource use and simplifying model loading/storage.\\n* **Llama.cpp:** An inferencing framework optimized by Oracle and Ampere specifically for Arm 64 CPUs to provide minimal setup and high performance.\\n* **OCI Ampere A1:** Arm-based compute shapes that support up to 80 OCPUs, with performance scaling linearly with core count.\\n\\n## Benchmarking Results\\nTesting was conducted using the **Phi-3-mini-4k-instruct-q4.gguf** model on an **A1 shape with 40 OCPUs** (Llama.cpp python version 0.2.78).\\n\\n| Processing Architecture | Prompt Tokens | Output Tokens | Inference Time (Sec) | Tokens Per Second (TPS) |\\n| :--- | :--- | :--- | :--- | :--- |\\n| Arm CPU A1 (40 OCPUs) | 10 | 50 | 1.257 | **39.77** |\\n| Arm CPU A1 (40 OCPUs) | 10 | 100 | 2.51 | **39.84** |\\n| Arm CPU A1 (40 OCPUs) | 10 | 200 | 5.119 | **39.07** |\\n| Arm CPU A1 (40 OCPUs) | 20 | 50 | 1.263 | **39.9** |\\n| Arm CPU A1 (40 OCPUs) | 30 | 200 | 5.227 | **38.27** |\\n\\n## Implementation Options in AI Quick Actions\\n\\nUsers can deploy GGUF models via two primary methods:\\n\\n### 1. Service-Cached Models\\nOracle provides pre-configured GGUF models that are ready for immediate deployment without downloading artifacts:\\n* `Phi-3-mini-4k-instruct-q4.gguf`\\n* `Phi-3-mini-4k-instruct-fp16.gguf`\\n\\n### 2. Bring Your Own Model (BYOM)\\nUsers can import any GGUF model from Hugging Face:\\n1. **Register** the model in AI Quick Actions.\\n2. **Deploy** by selecting the `llama.cpp` container under the **Inference Container** option in the **Advanced Options** section.\\n\\n> \\\"Enabling the use of A1 Ampere CPU shapes for (LLM) inferencing and retrieval-augmented generation (RAG) provides more options for our customers to work with LLMs for their use cases.\\\"\\n\\n## Additional Resources\\n* [OCI Data Science Sample Repository (GitHub)](https://github.com/oracle-samples/oci-data-science-ai-samples/)\\n* [AI Quick Actions Documentation](https://docs.oracle.com/en-us/iaas/data-science/using/ai-quick-actions.htm)\\n* **Contact:** ask-oci-data-science_grp@oracle.com\",\n \"error\": null\n },\n {\n \"url\": \"https://tiffena.me/blog/local-llm-inference-ampere-a1-linux/\",\n \"title\": \"Running LLMs locally on Ampere A1 Linux VM: Comparing options\",\n \"content\": \"# Running LLMs on Ampere A1 Linux (Oracle Cloud)\\n\\nThis guide evaluates options for running Large Language Models (LLMs) on **Oracle Cloud Infrastructure (OCI) Ampere A1 Flex** instances (specifically 4 ARM-based cores and 24GB RAM).\\n\\n## 🎯 The Goal & Hardware Constraints\\nThe objective is to achieve efficient, headless local inference on a Linux VM for a Python web app backend.\\n* **Hardware:** 4 Ampere A1 Cores, 24GB RAM (No GPU).\\n* **Target Performance:** >5 tokens per second (TPS) to match human reading speed.\\n* **Memory Availability:** ~20-22GB usable RAM after OS overhead.\\n\\n---\\n\\n## 🏆 The Verdict: Ampere-Optimized Ollama\\n**Ampere-Optimized Ollama** is the recommended choice for most users. It balances simplicity with significant performance gains specifically tuned for the Ampere Altra architecture.\\n\\n> \\\"It offers the best balance of simplicity (one-command install, automatic model handling), solid CPU performance on ARM, and manageable resource consumption.\\\"\\n\\n### Key Benefits:\\n* **Performance:** Specifically compiled to leverage Ampere Altra CPU architectural strengths.\\n* **Ease of Use:** Single command to pull and run models (`ollama run qwen2.5:3B`).\\n* **Integration:** Built-in OpenAI-compatible API on port `11434` and Python bindings.\\n* **Model Management:** Supports \\\"hot swapping\\\" models and uses `Modelfiles` for inference tweaks.\\n\\n---\\n\\n## 📊 Comparison of Inference Options\\n\\n| Tool | Best For | Pros | Cons |\\n| :--- | :--- | :--- | :--- |\\n| **Ampere Ollama** | **General Use** | Easiest setup; optimized for A1; model registry. | Higher abstraction overhead than llama.cpp. |\\n| **llama.cpp** | **Max Performance** | Lowest RAM usage; fastest raw speed; highly customizable. | Requires manual build; manual GGUF conversion. |\\n| **Docker Model Runner** | **Docker Workflows** | Native host performance; OCI artifact support. | Limited fine-grained control; newer/less tested. |\\n| **vLLM** | **High Throughput** | PagedAttention; great for concurrent users. | Overkill for single-user; CPU backend is slower. |\\n| **HF Transformers** | **Research/Dev** | Access to entire HF ecosystem; flexible. | High Python overhead; slowest CPU inference. |\\n\\n---\\n\\n## 💡 Model Selection & Performance\\nTo maintain a responsive experience (>5 TPS), quantization is mandatory (specifically **GGUF Q4_K_M** format).\\n\\n### Recommended Model Sizes:\\n* **7B - 8B Models (e.g., Llama 3.1, Mistral, Qwen2.5):** **The Sweet Spot.** Uses ~4-8GB RAM. High speed and plenty of room for context.\\n* **13B Models (e.g., Code Llama):** **Good Balance.** Uses ~7-10GB RAM. Better reasoning, still fits comfortably in 24GB.\\n* **34B+ Models:** **Not Recommended.** Uses ~15-25GB RAM. Leaves no room for OS/context; extremely slow TTFT (Time to First Token).\\n* **70B Models:** **Infeasible.** Exceeds the 24GB RAM limit.\\n\\n### Performance Estimates (4 Cores):\\n* **7B Model:** ~12–25 tokens/sec.\\n* **13B Model:** ~8–15 tokens/sec.\\n* **TTFT (Time to First Token):** 0.5s – 2.0s for optimized tools.\\n\\n---\\n\\n## 🛠️ Implementation Snippets\\n\\n### Running Ampere-Optimized Ollama (Docker)\\n```bash\\n# Pull and run the optimized container\\ndocker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama amperecomputingai/ollama\\n\\n# Interact via API\\ncurl http://localhost:11434/api/generate -d '{\\n \\\"model\\\": \\\"llama3\\\",\\n \\\"prompt\\\": \\\"Write a haiku about Ollama\\\"\\n}'\\n```\\n\\n### Running Hugging Face Transformers (Python)\\n```python\\nfrom transformers import pipeline; \\npipe = pipeline('text-generation', model='meta-llama/Llama-2-7b-hf', device='cpu')\\nprint(pipe(\\\"Write a haiku about Transformers\\\"))\\n```\\n\\n### vLLM (ARM64 Build)\\n```bash\\n# Start an OpenAI-compatible server\\npython -m vllm.entrypoints.openai.api_server \\\\\\n --model meta-llama/Llama-2-7b-hf \\\\\\n --tensor-parallel-size 1 \\\\\\n --dtype float16\\n```\\n\\n---\\n\\n## 📌 Key Takeaways\\n1. **Quantization is Essential:** Use GGUF format with 4-bit quantization to fit models into RAM and speed up CPU math.\\n2. **Avoid vLLM for Single Users:** While powerful for production, its CPU backend underperforms compared to `llama.cpp`-based tools on low-core counts.\\n3. **Monitor Resources:** Use `htop` or `free -h` to ensure the model doesn't trigger \\\"swapping,\\\" which causes performance to collapse.\",\n \"error\": null\n }\n ]\n}",
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"content": "Sí, **te alcanza**, pero con una salvedad importante: **Oracle Always Free sirve para correr modelos chicos/medianos cuantizados en CPU, no para “un ChatGPT propio” pesado**.\n\n## Qué te da Oracle Always Free\nSegún la doc de OCI, el tier gratis incluye aprox.:\n\n- **Hasta 4 OCPU ARM (Ampere A1)**\n- **24 GB de RAM**\n- **200 GB de storage total**\n- sin GPU\n\nEso significa:\n\n- **bien** para inferencia local en **CPU**\n- **mal** para modelos grandes o muchas requests concurrentes\n- **obligatorio** usar modelos **quantized/GGUF** y runners tipo **llama.cpp** u **Ollama optimizado para Ampere**\n\n## Mi conclusión corta\n**Sí, es suficiente si tu objetivo es:**\n\n- tener una API local tuya\n- usar un modelo para chat, automatizaciones, clasificación, extracción, RAG liviano\n- uno o pocos usuarios\n- tolerar velocidades tipo “usable” pero no brutales\n\n**No es suficiente si querés:**\n\n- modelos grandes tipo **70B**\n- mucha concurrencia\n- latencia muy baja estilo SaaS comercial\n- servir algo “pro” a varios usuarios a la vez\n\n---\n\n## Qué modelos sí veo razonables ahí\n\n### Recomendados\nEstos me parecen los más sensatos para ese hardware:\n\n- **Qwen2.5 3B Instruct**\n- **Qwen2.5 7B Instruct**\n- **Llama 3.2 3B**\n- **Phi-3 Mini / 3.8B**\n- **Mistral 7B Instruct**\n- **Gemma 2 2B / 9B** (9B ya más justo, pero posible)\n\nEn **GGUF Q4_K_M** o parecido.\n\n### Posibles pero ya más al límite\n- **13B-ish** cuantizados \n Entran, pero ya empezás a pagar con:\n - más latencia\n - peor tiempo al primer token\n - menos margen de RAM para contexto y servicios auxiliares\n\n### No\n- **34B+**: mala idea\n- **70B**: directamente **no**\n- cualquier cosa sin cuantizar: tampoco\n\n---\n\n## Rendimiento esperable\nEncontré referencias bastante alineadas con esto para un A1 4 cores / 24 GB:\n\n- **7B quantized:** aprox. **12–25 tok/s**\n- **13B quantized:** aprox. **8–15 tok/s**\n- target de “usable para un humano”: arriba de **5 tok/s**\n\nEso, en la práctica, significa que un **3B o 7B bien elegido** puede quedar **bastante decente** para uso personal.\n\nIgual te diría algo importante: \n**para tareas de agente / tools / automatización, muchas veces un 3B–7B bien afinado rinde mejor de lo que uno imagina** si el prompting está bien y no le pedís magia.\n\n---\n\n## Lo que más te conviene correr ahí\nSi yo tuviera que elegir hoy para Oracle Free, haría esto:\n\n### Opción 1 — la más sensata\n**Qwen2.5 7B Instruct Q4_K_M** con **llama.cpp** o **Ollama optimizado para Ampere**\n\nPor qué:\n- buen equilibrio entre calidad y RAM\n- bastante versátil\n- razonable para chat, scripting, extracción, tareas backend\n\n### Opción 2 — si querés más velocidad\n**Qwen2.5 3B** o **Llama 3.2 3B**\n\nPor qué:\n- más rápido\n- menos RAM\n- mejor para automatizaciones o fallback local\n\n### Opción 3 — si querés coding liviano\n**Qwen2.5-Coder 7B** cuantizado \nPuede andar, pero no esperes nivel Claude/GPT en programación compleja.\n\n---\n\n## Tooling: qué usaría yo\n### Mejor opción\n- **llama.cpp**\n - más eficiente en CPU\n - ideal para GGUF\n - muy buena opción en ARM\n\n### Más cómodo\n- **Ollama optimizado para Ampere**\n - más simple de instalar y usar\n - API fácil\n - un poco más de overhead, pero práctico\n\n### Lo que evitaría en ese contexto\n- **vLLM** en CPU para un solo usuario: no me parece la mejor apuesta\n- Transformers “pelado” en Python: más overhead, peor experiencia\n\n---\n\n## Limitaciones reales del Always Free\nHay varias que te conviene tener en cuenta:\n\n### 1. Es ARM, no x86\nNo todo corre igual de cómodo. \nTenés que elegir tooling compatible con **ARM64**.\n\n### 2. Oracle puede reclamar instancias idle\nLa doc dice que pueden recuperar instancias Always Free si quedan muy ociosas durante varios días.\n\n### 3. Storage finito\nCon 200 GB estás bien, pero no para bajar media Hugging Face.\nUn par de modelos GGUF sí; una colección enorme, no.\n\n### 4. Cero GPU\nTodo depende de CPU + quantization + paciencia.\n\n---\n\n## Veredicto honesto\n**Para uso personal / experimental / backend chico: sí, re sirve.** \n**Para reemplazar un servicio comercial potente: no.**\n\nSi me pedís una recomendación concreta:\n\n> **Sí vale la pena** montar una instancia local en Oracle Always Free si querés correr **Qwen2.5 3B/7B, Llama 3.2 3B o Mistral 7B cuantizados** con **llama.cpp/Ollama**.\n\nEs probablemente el **mejor uso posible** de ese tier gratis para LLM local.\n\nSi querés, en el próximo mensaje te hago una **shortlist exacta de 3 modelos** para tu caso de uso:\n1. **chat general**\n2. **coding**\n3. **automatizaciones / agentes** \ny te digo cuál elegiría yo en cada uno.",
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"content": "Salió Gemma 4 Hoy"
},
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"arguments": "{\"query\": \"Gemma 4 announcement April 2026 model sizes specs\"}"
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"content": "{\n \"success\": true,\n \"data\": {\n \"web\": [\n {\n \"title\": \"Google announces Gemma 4 open AI models, switches to Apache ...\",\n \"url\": \"https://arstechnica.com/ai/2026/04/google-announces-gemma-4-open-ai-models-switches-to-apache-2-0-license/\",\n \"description\": \"# Google announces Gemma 4 open AI models, switches to Apache 2.0 license. Gemma 4 brings the first major update to Google’s open models in a year. Like past versions of its open-weight models, Google has designed Gemma 4 to be usable on local machines. Google says Gemma 31B will debut at number three on the Arena list of top open AI models, behind GLM-5 and Kimi 2.5. Based on the same underlying technology as Google’s Gemini 3 closed models, Gemma 4 offers improved reasoning, math, and instruction-following. Google Pixels and a few other phones run local AI models known as Gemini Nano. A Google representative notes that Gemini Nano has always been derived from Gemma models, but that’s especially true of the next-gen update to Gemini Nano 4. The current Gemini Nano 3 running on Pixel phones is based on Gemma 3n, but Google confirmed to Ars Technica that the next-gen Nano 4 will have 2B and 4B variants based on Gemma 4 E2B and E4B.\",\n \"position\": 1\n },\n {\n \"title\": \"Google releases Gemma 4, a family of open models built ... - Engadget\",\n \"url\": \"https://www.engadget.com/ai/google-releases-gemma-4-a-family-of-open-models-built-off-of-gemini-3-160000332.html\",\n \"description\": \"[News](https://www.engadget.com/news/). [Reviews](https://www.engadget.com/reviews/). [Buying guides](https://www.engadget.com/best-tech/). [Gaming](https://www.engadget.com/gaming/). [Big tech](https://www.engadget.com/big-tech/). [Deals](https://www.engadget.com/deals/). [Entertainment](https://www.engadget.com/entertainment/). [More](https://www.engadget.com/ai/google-releases-gemma-4-a-family-of-open-models-built-off-of-gemini-3-160000332.html). * [New York Auto Show 2026: EV roundup](https://www.engadget.com/transportation/evs/the-hottest-evs-from-the-2026-new-york-auto-show-plus-one-brawny-concept-120000557.html). * [What’s going on with Donut Lab'?](https://www.engadget.com/transportation/evs/whats-going-on-with-donut-lab-173007121.html). * [Apple AirPods Max 2 review](https://www.engadget.com/audio/headphones/apple-airpods-max-2-review-better-late-than-never-130000982.html). * [WWDC 2026: What to expect](https://www.engadget.com/big-tech/what-to-expect-from-wwdc-2026-110000086.html). * [March Madness 2026](https://www.engadget.com/entertainment/streaming/march-madness-2026-how-to-watch-the-sweet-16-and-elite-eight-this-weekend-154903032.html). 1. [](https://www.engadget.com/ai/google-releases-gemma-4-a-family-of-open-models-built-off-of-gemini-3-160000332.html#). 2. [AI](https://www.engadget.com/ai/). [Igor Bonifacic](https://www.engadget.com/about/editors/igor-bonifacic/). [Add Engadget on Google](https://www.google.com/preferences/source?q=engadget.com). When Google released [Gemini 3 Pro](https://www.engadget.com/ai/googles-new-gemini-3-model-arrives-in-ai-mode-and-the-gemini-app-160054273.html) at the end of last year, it was a significant step forward for the company's proprietary large language models. * [Privacy Dashboard](https://guce.engadget.com/privacy-dashboard?locale=en-US). [Engadget masthead](https://www.engadget.com/about/). [About our ads](https://www.engadget.com/about-our-advertising/). [Advertise](https://www.engadget.com/about/advertise/). [Licensing](https://info.wrightsmedia.com/engadget-licensing). [FAQ](https://www.engadget.com/about-faq-194611880.html). [RSS feed](https://www.engadget.com/rss.xml). [Reviews](https://www.engadget.com/reviews/). [Gear](https://www.engadget.com/gear/). [Gaming](https://www.engadget.com/gaming/). [Entertainment](https://www.engadget.com/entertainment/). [Tomorrow](https://www.engadget.com/tomorrow/). [Buying guides](https://www.engadget.com/best-tech/). [Video](https://www.engadget.com/videos/). [Podcasts](https://www.engadget.com/podcasts/). [Deals](https://www.engadget.com/deals/). [Comment guidelines](https://www.engadget.com/2017-05-01-engadget-commenting-policy.html). [Support](https://www.engadget.com/support/). [Best laptop](https://www.engadget.com/computing/laptops/best-laptops-120008636.html). [The best iPad](https://www.engadget.com/mobile/tablets/best-ipads-how-to-pick-the-best-apple-tablet-for-you-150054066.html). [Best Bluetooth speaker](https://www.engadget.com/audio/speakers/best-portable-bluetooth-speakers-133004551.html). [Best E Ink tablets](https://www.engadget.com/mobile/tablets/best-e-ink-tablet-130037939.html). [Best wireless earbuds](https://www.engadget.com/audio/headphones/best-wireless-earbuds-120058222.html). [Best power banks](https://www.engadget.com/computing/accessories/best-power-bank-143048526.html). [Best gaming handhelds](https://www.engadget.com/gaming/best-handheld-gaming-system-140018863.html). [Comment guidelines](https://www.engadget.com/2017-05-01-engadget-commenting-policy.html). [Support](https://www.engadget.com/support/). [Best laptop](https://www.engadget.com/computing/laptops/best-laptops-120008636.html). [The best iPad](https://www.engadget.com/mobile/tablets/best-ipads-how-to-pick-the-best-apple-tablet-for-you-150054066.html). [Best Bluetooth speaker](https://www.engadget.com/audio/speakers/best-portable-bluetooth-speakers-133004551.html). [Best E Ink tablets](https://www.engadget.com/mobile/tablets/best-e-ink-tablet-130037939.html). [Best wireless earbuds](https://www.engadget.com/audio/headphones/best-wireless-earbuds-120058222.html). [Best power banks](https://www.engadget.com/computing/accessories/best-power-bank-143048526.html). [Best gaming handhelds](https://www.engadget.com/gaming/best-handheld-gaming-system-140018863.html). [](https://www.facebook.com/Engadget/)[](https://twitter.com/engadget/)[](https://www.instagram.com/engadget/). [About Us](https://legal.yahoo.com/us/en/yahoo/privacy/guce/faq/index.html)[Reprints and Permissions](https://info.wrightsmedia.com/engadget-licensing)[](https://www.engadget.com/ai/google-releases-gemma-4-a-family-of-open-models-built-off-of-gemini-3-160000332.html)[Trademarks](https://legal.aol.com/trademarks/)[Advertise](https://www.engadget.com/about/advertise/)[About Our Ads](https://legal.yahoo.com/us/en/yahoo/privacy/adinfo/index.html). [Terms](https://guce.engadget.com/terms?locale=en-US). [Privacy Policy](https://guce.engadget.com/privacy-policy?locale=en-US).\",\n \"position\": 2\n },\n {\n \"title\": \"Gemma 4 - Models - Google DeepMind\",\n \"url\": \"https://deepmind.google/models/gemma/gemma-4/\",\n \"description\": \"[ Genie 3 Generate and explore interactive worlds](https://deepmind.google/models/genie/)[ Gemini Robotics Perceive, reason, use tools and interact](https://deepmind.google/models/gemini-robotics/). [ Gemma Build responsible AI applications at scale](https://deepmind.google/models/gemma/). [ SIMA 2 An agent that plays, reasons, and learns with you](https://deepmind.google/blog/sima-2-an-agent-that-plays-reasons-and-learns-with-you-in-virtual-3d-worlds/)[ Genie 3 Generate and explore interactive worlds](https://deepmind.google/models/genie/)[ AlphaGo Mastering the game of Go](https://deepmind.google/research/alphago/)[ Gemini Robotics Perceive, reason, use tools and interact](https://deepmind.google/models/gemini-robotics/). [ AlphaEarth Foundations Map our planet in unprecedented detail](https://deepmind.google/blog/alphaearth-foundations-helps-map-our-planet-in-unprecedented-detail/)[ WeatherNext Fast and accurate AI weather forecasting](https://deepmind.google/science/weathernext/)[ Weather Lab Test our experimental weather models](https://deepmind.google.com/science/weatherlab/?utm_source=deepmind.google&utm_medium=referral&utm_campaign=gdm&utm_content=). [ Genie 3 Generate and explore interactive worlds](https://deepmind.google/models/genie/)[ Gemini Robotics Perceive, reason, use tools and interact](https://deepmind.google/models/gemini-robotics/). [ Gemma Build responsible AI applications at scale](https://deepmind.google/models/gemma/). [ SIMA 2 An agent that plays, reasons, and learns with you](https://deepmind.google/blog/sima-2-an-agent-that-plays-reasons-and-learns-with-you-in-virtual-3d-worlds/)[ Genie 3 Generate and explore interactive worlds](https://deepmind.google/models/genie/)[ AlphaGo Mastering the game of Go](https://deepmind.google/research/alphago/)[ Gemini Robotics Perceive, reason, use tools and interact](https://deepmind.google/models/gemini-robotics/). [Try in Google AI Studio](https://aistudio.google.com/prompts/new_chat?model=gemma-4-31b-it&utm_source=deepmind.google&utm_medium=referral&utm_campaign=gdm&utm_content=).\",\n \"position\": 3\n },\n {\n \"title\": \"Gemma 4 model card | Google AI for Developers\",\n \"url\": \"https://ai.google.dev/gemma/docs/core/model_card_4\",\n \"description\": \"# Gemma 4 model card. multimodal, handling text and image input (with audio supported on small models). window, while the medium models support 256K. ### Dense Models. Gemma 4 models handle a broad range of tasks across text, vision, and audio. You can use all Gemma 4 models with the latest version of Transformers. MODEL_ID = \\\"google/gemma-4-E2B-it\\\". Once the model is loaded, you can start generating output:. outputs = model.generate(**inputs, max_new_tokens=1024). Compared to Gemma 3, the models use standard `system`, `assistant`, and `user`. All models support image inputs and can process videos as frames whereas the E2B. and E4B models also support audio inputs. ## **Model Data**. Data used for model training and how the data was processed. Gemma 4 models were developed in partnership with internal safety and. content safety relative to previous Gemma models. significantly outperform Gemma 3 and 3n models in improving safety, while. Multimodal models (capable of processing vision, language, and/or audio) have a.\",\n \"position\": 4\n },\n {\n \"title\": \"Google releases Gemma 4 under Apache 2.0 - VentureBeat\",\n \"url\": \"https://venturebeat.com/technology/google-releases-gemma-4-under-apache-2-0-and-that-license-change-may-matter\",\n \"description\": \"As some Chinese AI labs (most notably Alibaba’s latest Qwen models, Qwen3.5 Omni and Qwen 3.6 Plus) have begun pulling back from fully open releases for their latest models, Google is moving in the opposite direction — opening up its most capable Gemma release yet while explicitly stating the architecture draws from its commercial **Gemini 3** research. The \\\"workstation\\\" tier includes a **31B-parameter dense model** and a **26B A4B Mixture-of-Experts model** — both supporting text and image input with 256K-token context windows. For teams building voice-first applications that need to keep data local — think healthcare, field service, or multilingual customer interaction — running ASR, translation, reasoning, and function calling in a single model on a phone or edge device is a genuine architectural simplification. What distinguishes Gemma 4 is less any single benchmark and more the combination: strong reasoning, native multimodality across text, vision, and audio, function calling, 256K context, and a genuinely permissive license — all in a single model family with deployment options from edge devices to cloud serverless.\",\n \"position\": 5\n }\n ]\n }\n}",
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"content": "{\n \"success\": true,\n \"data\": {\n \"web\": [\n {\n \"title\": \"Google releases Gemma 4, a family of open models built ... - Engadget\",\n \"url\": \"https://www.engadget.com/ai/google-releases-gemma-4-a-family-of-open-models-built-off-of-gemini-3-160000332.html\",\n \"description\": \"[News](https://www.engadget.com/news/). [Reviews](https://www.engadget.com/reviews/). [Buying guides](https://www.engadget.com/best-tech/). [Gaming](https://www.engadget.com/gaming/). [Big tech](https://www.engadget.com/big-tech/). [Deals](https://www.engadget.com/deals/). [Entertainment](https://www.engadget.com/entertainment/). [More](https://www.engadget.com/ai/google-releases-gemma-4-a-family-of-open-models-built-off-of-gemini-3-160000332.html). * [New York Auto Show 2026: EV roundup](https://www.engadget.com/transportation/evs/the-hottest-evs-from-the-2026-new-york-auto-show-plus-one-brawny-concept-120000557.html). * [What’s going on with Donut Lab'?](https://www.engadget.com/transportation/evs/whats-going-on-with-donut-lab-173007121.html). * [Apple AirPods Max 2 review](https://www.engadget.com/audio/headphones/apple-airpods-max-2-review-better-late-than-never-130000982.html). * [WWDC 2026: What to expect](https://www.engadget.com/big-tech/what-to-expect-from-wwdc-2026-110000086.html). * [March Madness 2026](https://www.engadget.com/entertainment/streaming/march-madness-2026-how-to-watch-the-sweet-16-and-elite-eight-this-weekend-154903032.html). 1. [](https://www.engadget.com/ai/google-releases-gemma-4-a-family-of-open-models-built-off-of-gemini-3-160000332.html#). 2. [AI](https://www.engadget.com/ai/). [Igor Bonifacic](https://www.engadget.com/about/editors/igor-bonifacic/). [Add Engadget on Google](https://www.google.com/preferences/source?q=engadget.com). When Google released [Gemini 3 Pro](https://www.engadget.com/ai/googles-new-gemini-3-model-arrives-in-ai-mode-and-the-gemini-app-160054273.html) at the end of last year, it was a significant step forward for the company's proprietary large language models. * [Privacy Dashboard](https://guce.engadget.com/privacy-dashboard?locale=en-US). [Engadget masthead](https://www.engadget.com/about/). [About our ads](https://www.engadget.com/about-our-advertising/). [Advertise](https://www.engadget.com/about/advertise/). [Licensing](https://info.wrightsmedia.com/engadget-licensing). [FAQ](https://www.engadget.com/about-faq-194611880.html). [RSS feed](https://www.engadget.com/rss.xml). [Reviews](https://www.engadget.com/reviews/). [Gear](https://www.engadget.com/gear/). [Gaming](https://www.engadget.com/gaming/). [Entertainment](https://www.engadget.com/entertainment/). [Tomorrow](https://www.engadget.com/tomorrow/). [Buying guides](https://www.engadget.com/best-tech/). [Video](https://www.engadget.com/videos/). [Podcasts](https://www.engadget.com/podcasts/). [Deals](https://www.engadget.com/deals/). [Comment guidelines](https://www.engadget.com/2017-05-01-engadget-commenting-policy.html). [Support](https://www.engadget.com/support/). [Best laptop](https://www.engadget.com/computing/laptops/best-laptops-120008636.html). [The best iPad](https://www.engadget.com/mobile/tablets/best-ipads-how-to-pick-the-best-apple-tablet-for-you-150054066.html). [Best Bluetooth speaker](https://www.engadget.com/audio/speakers/best-portable-bluetooth-speakers-133004551.html). [Best E Ink tablets](https://www.engadget.com/mobile/tablets/best-e-ink-tablet-130037939.html). [Best wireless earbuds](https://www.engadget.com/audio/headphones/best-wireless-earbuds-120058222.html). [Best power banks](https://www.engadget.com/computing/accessories/best-power-bank-143048526.html). [Best gaming handhelds](https://www.engadget.com/gaming/best-handheld-gaming-system-140018863.html). [Comment guidelines](https://www.engadget.com/2017-05-01-engadget-commenting-policy.html). [Support](https://www.engadget.com/support/). [Best laptop](https://www.engadget.com/computing/laptops/best-laptops-120008636.html). [The best iPad](https://www.engadget.com/mobile/tablets/best-ipads-how-to-pick-the-best-apple-tablet-for-you-150054066.html). [Best Bluetooth speaker](https://www.engadget.com/audio/speakers/best-portable-bluetooth-speakers-133004551.html). [Best E Ink tablets](https://www.engadget.com/mobile/tablets/best-e-ink-tablet-130037939.html). [Best wireless earbuds](https://www.engadget.com/audio/headphones/best-wireless-earbuds-120058222.html). [Best power banks](https://www.engadget.com/computing/accessories/best-power-bank-143048526.html). [Best gaming handhelds](https://www.engadget.com/gaming/best-handheld-gaming-system-140018863.html). [](https://www.facebook.com/Engadget/)[](https://twitter.com/engadget/)[](https://www.instagram.com/engadget/). [About Us](https://legal.yahoo.com/us/en/yahoo/privacy/guce/faq/index.html)[Reprints and Permissions](https://info.wrightsmedia.com/engadget-licensing)[](https://www.engadget.com/ai/google-releases-gemma-4-a-family-of-open-models-built-off-of-gemini-3-160000332.html)[Trademarks](https://legal.aol.com/trademarks/)[Advertise](https://www.engadget.com/about/advertise/)[About Our Ads](https://legal.yahoo.com/us/en/yahoo/privacy/adinfo/index.html). [Terms](https://guce.engadget.com/terms?locale=en-US). [Privacy Policy](https://guce.engadget.com/privacy-policy?locale=en-US).\",\n \"position\": 1\n },\n {\n \"title\": \"Google unveils Gemma 4 models, aimed at advanced reasoning ...\",\n \"url\": \"https://www.msn.com/en-us/news/technology/google-unveils-gemma-4-models-aimed-at-advanced-reasoning-agentic-workflows/ar-AA200OrS\",\n \"description\": \"Book with Early 2026 Deals and stay until April. Sponsored. In ... Google releases Gemma 4, a family of open models built off of Gemini 3.\",\n \"position\": 2\n },\n {\n \"title\": \"The latest AI news we announced in March 2026 - Google Blog\",\n \"url\": \"https://blog.google/innovation-and-ai/technology/ai/google-ai-updates-march-2026/\",\n \"description\": \"[Skip to main content](https://blog.google/innovation-and-ai/technology/ai/google-ai-updates-march-2026/#jump-content). * [Gemini models](https://blog.google/innovation-and-ai/models-and-research/gemini-models/). * [See all](https://blog.google/innovation-and-ai/models-and-research/). * [Gemini app](https://blog.google/innovation-and-ai/products/gemini-app/). * [See all](https://blog.google/innovation-and-ai/products/). * [See all](https://blog.google/innovation-and-ai/infrastructure-and-cloud/). [See all AI updates](https://blog.google/innovation-and-ai/). * [Gemini models](https://blog.google/innovation-and-ai/models-and-research/gemini-models/). * [Gemini app](https://blog.google/innovation-and-ai/products/gemini-app/). [See all](https://blog.google/innovation-and-ai/products/ \\\"See all Products articles\\\"). [See all AI updates](https://blog.google/innovation-and-ai/). * [Health](https://blog.google/innovation-and-ai/technology/health/). * [Health](https://blog.google/innovation-and-ai/technology/health/). * [How is Gemini changing Maps?](https://blog.google/innovation-and-ai/technology/ai/google-ai-updates-march-2026/#). * [Gemini models](https://blog.google/innovation-and-ai/models-and-research/gemini-models/). * [See all](https://blog.google/innovation-and-ai/models-and-research/). * [Gemini app](https://blog.google/innovation-and-ai/products/gemini-app/). * [See all](https://blog.google/innovation-and-ai/products/). * [See all](https://blog.google/innovation-and-ai/infrastructure-and-cloud/). [See all AI updates](https://blog.google/innovation-and-ai/). * [Health](https://blog.google/innovation-and-ai/technology/health/). 3. [Technology](https://blog.google/innovation-and-ai/technology/). [**We introduced tools to help people switch to Gemini**](https://blog.google/innovation-and-ai/products/gemini-app/switch-to-gemini-app/). [**We released new AI features for your devices in the March Pixel Drop**](https://blog.google/products-and-platforms/devices/pixel/march-2026-pixel-drop/). * [Health](https://blog.google/innovation-and-ai/technology/health/). [ Developer tools #### Gemma 4: Byte for byte, the most capable open models By Clement Farabet & Olivier Lacombe Apr 02, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/). [ Developer tools #### New ways to balance cost and reliability in the Gemini API By Lucia Loher & Hussein Hassan Harrirou Apr 02, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/introducing-flex-and-priority-inference/). By Trey Nguyen Apr 01, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/gemini-api-docsmcp-agent-skills/).\",\n \"position\": 3\n },\n {\n \"title\": \"Google's Gemma 4 model goes fully open-source and ... - ZDNET\",\n \"url\": \"https://www.zdnet.com/article/google-gemma-4-fully-open-source-powerful-local-ai/\",\n \"description\": \"# Google's Gemma 4 model goes fully open-source and unlocks powerful local AI - even on phones. Google's Gemma 4 model goes fully open-source and unlocks powerful local AI - even on phones. Google announced today that its DeepMind AI research division is releasing Gemma 4, its latest generation of open large language models. The models are being released under the Apache 2.0 license, making them truly open source compared to the permissive but still controlled license of earlier Gemma generations. The difference is that Gemini is a subscription-based closed product, whereas Gemma is an open model that can be downloaded and run locally for free. Instead, they're licensing Gemma 4 under the Apache 2.0 license, which means users and developers can use and distribute the model in *any* way they want without restrictions. Not only can we expect to see Gemma 4 adopted in more projects, but it's also now legitimately possible to bundle the AI with products, services, and devices that can benefit from a powerful on-board model.\",\n \"position\": 4\n },\n {\n \"title\": \"Google's Gemma 4 Surfaces Online Ahead of Official Release\",\n \"url\": \"https://www.youtube.com/watch?v=1NoLvfs15Fk\",\n \"description\": \"Google DeepMind's next open-source AI model — Gemma 4 — has been spotted in LMSYS Chatbot Arena before an official announcement.\",\n \"position\": 5\n }\n ]\n }\n}",
"tool_call_id": "call_1aafb582048e45aeaf9d6c9a"
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{
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"content": "",
"tool_calls": [
{
"id": "call_2a6a740650534189830d7ecd",
"call_id": "call_2a6a740650534189830d7ecd",
"response_item_id": "fc_2a6a740650534189830d7ecd",
"type": "function",
"function": {
"name": "web_extract",
"arguments": "{\"urls\": \n[\"https://arstechnica.com/ai/2026/04/google-announces-gemma-4-open-ai-models-switches-to-apache-2-0-license/\", \"https://ai.google.dev/gemma/docs/core/model_card_4\"]\n\n}"
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{
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"call_id": "call_849646efc5054fedad6ad1ec",
"response_item_id": "fc_849646efc5054fedad6ad1ec",
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"name": "web_search",
"arguments": "{\"query\": \"Gemma 4 E2B E4B 31B model sizes parameters details 2026\"}"
}
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],
"reasoning": "Let me get more details on the specific Gemma 4 model sizes to assess which ones would fit on the Oracle Always Free tier (24GB RAM, 4 ARM cores)."
},
{
"role": "tool",
"content": "{\n \"results\": [\n {\n \"url\": \"https://arstechnica.com/ai/2026/04/google-announces-gemma-4-open-ai-models-switches-to-apache-2-0-license/\",\n \"title\": \"Google announces Gemma 4 open AI models, switches to Apache ...\",\n \"content\": \"# Google Announces Gemma 4: Open Models & Apache 2.0 License\\n\\nGoogle has unveiled **Gemma 4**, the first major update to its open-weight model family in over a year. This release marks a significant shift in strategy, moving away from restrictive custom licenses toward the industry-standard **Apache 2.0 license**.\\n\\n## 1. The Gemma 4 Model Lineup\\nGemma 4 is designed for local execution across a range of hardware, from high-end GPUs to mobile devices.\\n\\n| Model Variant | Size/Type | Target Hardware | Key Feature |\\n| :--- | :--- | :--- | :--- |\\n| **31B Dense** | 31B Parameters | 80GB Nvidia H100 (unquantized) | Highest quality; optimized for fine-tuning. |\\n| **26B MoE** | 26B (3.8B active) | 80GB Nvidia H100 (unquantized) | High tokens-per-second via Mixture of Experts. |\\n| **Effective 4B (E4B)** | 4B Parameters | Mobile, Raspberry Pi, Jetson Nano | Balanced performance for edge devices. |\\n| **Effective 2B (E2B)** | 2B Parameters | Mobile, Raspberry Pi, Jetson Nano | Lowest memory/battery usage; \\\"near-zero\\\" latency. |\\n\\n> **Note on Hardware:** While the larger models are designed for the $20,000 H100 in bfloat16 format, they can fit on consumer GPUs if quantized to lower precision.\\n\\n## 2. Performance & Capabilities\\nGemma 4 is built on the same technology as the closed **Gemini 3** models, offering improvements in reasoning, math, and instruction-following.\\n\\n* **Benchmark Ranking:** The 31B model is expected to debut at **#3 on the Arena list** of open models, trailing only GLM-5 and Kimi 2.5 (both of which are significantly larger).\\n* **Context Windows:** \\n * **Edge Models (2B/4B):** 128k tokens.\\n * **Large Models (26B/31B):** 256k tokens.\\n* **Agentic Workflows:** Native support for function calling, structured JSON output, and API instructions.\\n* **Multimodal & Coding:** Optimized for offline code generation, OCR, chart understanding, and native speech recognition.\\n* **Language Support:** Works in over 140 languages.\\n\\n## 3. The Licensing Shift: Apache 2.0\\nIn a major win for developers, Google has abandoned its restrictive custom license in favor of **Apache 2.0**.\\n\\n* **Previous Issues:** The Gemma 3 license allowed Google to update terms unilaterally, restricted commercial use, and had ambiguous clauses regarding synthetic data ownership.\\n* **Apache 2.0 Benefits:** Permissive, familiar to developers, no overbearing terms of use, and no commercial restrictions.\\n\\n## 4. Future of Mobile: Gemini Nano 4\\nGoogle confirmed that **Gemini Nano 4** (the on-device AI for Pixel phones) will be derived directly from the Gemma 4 E2B and E4B models.\\n* **Forward Compatibility:** Developers can prototype agentic workflows in the **AI Core Developer Preview** using Gemma E2B/E4B today; these will be compatible with Nano 4 upon its official launch.\\n\\n## 5. Availability\\nThe models are available immediately through the following channels:\\n* **Testing:** [AI Studio](https://aistudio.google.com/) (31B and 26B MoE) and [AI Edge Gallery](https://ai.google.dev/edge) (E4B and E2B).\\n* **Downloads:** [Hugging Face](https://huggingface.co/collections/google/gemma-4), [Kaggle](https://www.kaggle.com/models/google/gemma-4), and [Ollama](https://ollama.com/library/gemma4).\\n* **Cloud:** Available for deployment on Google Cloud (paid).\",\n \"error\": null\n },\n {\n \"url\": \"https://ai.google.dev/gemma/docs/core/model_card_4\",\n \"title\": \"Gemma 4 model card | Google AI for Developers\",\n \"content\": \"# Gemma 4 Model Card Summary\\n\\nGemma 4 is a family of multimodal open models developed by **Google DeepMind**. Built on both Dense and Mixture-of-Experts (MoE) architectures, these models handle text and image inputs (with audio on smaller variants) and feature a context window of up to **256K tokens** with support for **140+ languages**.\\n\\n## **Key Architectural Advancements**\\n* **Reasoning:** Configurable \\\"thinking modes\\\" for complex problem-solving.\\n* **Multimodality:** Native support for Text and Image (all models), plus Video and Audio (E2B and E4B models).\\n* **Hybrid Attention:** Interleaves local sliding window attention with global attention for speed and memory efficiency.\\n* **Per-Layer Embeddings (PLE):** Used in smaller models to maximize parameter efficiency for on-device deployment.\\n* **Native System Prompt:** Introduces the `system` role for structured conversation control.\\n\\n---\\n\\n## **Model Variants & Specifications**\\n\\n### **Dense Models**\\n| Property | E2B | E4B | 31B Dense |\\n| :--- | :--- | :--- | :--- |\\n| **Effective Params** | 2.3B (5.1B total) | 4.5B (8B total) | 30.7B |\\n| **Context Length** | 128K tokens | 128K tokens | 256K tokens |\\n| **Modalities** | Text, Image, Audio | Text, Image, Audio | Text, Image |\\n| **Target Device** | High-end phones | Laptops | Workstations/Servers |\\n\\n### **Mixture-of-Experts (MoE) Model**\\n* **Model:** **26B A4B MoE**\\n* **Active Parameters:** 3.8B (runs at the speed of a 4B model).\\n* **Total Parameters:** 25.2B.\\n* **Experts:** 8 active / 128 total + 1 shared.\\n* **Context:** 256K tokens.\\n\\n---\\n\\n## **Benchmark Performance Highlights**\\nGemma 4 shows significant leaps over Gemma 3, particularly in reasoning and coding.\\n\\n| Metric | Gemma 4 31B | Gemma 4 26B A4B | Gemma 3 27B |\\n| :--- | :--- | :--- | :--- |\\n| **MMLU Pro** | 85.2% | 82.6% | 67.6% |\\n| **AIME 2026 (no tools)** | 89.2% | 88.3% | 20.8% |\\n| **LiveCodeBench v6** | 80.0% | 77.1% | 29.1% |\\n| **Codeforces ELO** | 2150 | 1718 | 110 |\\n| **MATH-Vision** | 85.6% | 82.4% | 46.0% |\\n\\n---\\n\\n## **Implementation & Code Snippets**\\n\\n### **Installation**\\n```bash\\npip install -U transformers torch accelerate\\n```\\n\\n### **Basic Usage (Text Generation)**\\n```python\\nimport torch\\nfrom transformers import AutoProcessor, AutoModelForCausalLM\\n\\nMODEL_ID = \\\"google/gemma-4-E2B-it\\\"\\nprocessor = AutoProcessor.from_pretrained(MODEL_ID)\\nmodel = AutoModelForCausalLM.from_pretrained(MODEL_ID, dtype=torch.bfloat16, device_map=\\\"auto\\\")\\n\\nmessages = [\\n {\\\"role\\\": \\\"system\\\", \\\"content\\\": \\\"You are a helpful assistant.\\\"},\\n {\\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"Write a short joke about saving RAM.\\\"},\\n]\\n\\ntext = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)\\ninputs = processor(text=text, return_tensors=\\\"pt\\\").to(model.device)\\noutputs = model.generate(**inputs, max_new_tokens=1024)\\n```\\n\\n---\\n\\n## **Best Practices & Configurations**\\n\\n### **1. Sampling Parameters**\\nFor optimal performance, use these standardized settings:\\n* `temperature=1.0`\\n* `top_p=0.95`\\n* `top_k=64`\\n\\n### **2. Thinking Mode**\\nTo enable reasoning, set `enable_thinking=True`. The model uses specific control tokens:\\n* `<|think|>`\\n* `<|channel>thought\\\\n`\\n* ``\\n\\n### **3. Multimodal Constraints**\\n* **Audio:** Maximum length of **30 seconds**.\\n* **Video:** Maximum length of **60 seconds** (processed at 1 frame per second).\\n* **Image Resolution:** Supports variable resolution via a \\\"visual token budget\\\" to balance detail vs. compute.\\n\\n---\\n\\n## **Ethics, Safety, and Data**\\n* **Training Data:** Diverse collection of web docs, code, images, and audio with a cutoff of **January 2025**.\\n* **Safety:** Undergoes the same rigorous evaluations as Gemini models. Gemma 4 shows \\\"major improvements\\\" in content safety and lower unjustified refusal rates compared to Gemma 3.\\n* **License:** [Apache 2.0](https://ai.google.dev/gemma/docs/gemma_4_license).\\n* **Limitations:** Users should be aware of potential hallucinations, the need for task-specific fine-tuning, and inherent biases in large-scale datasets.\",\n \"error\": null\n }\n ]\n}",
"tool_call_id": "call_2a6a740650534189830d7ecd"
},
{
"role": "tool",
"content": "{\n \"success\": true,\n \"data\": {\n \"web\": [\n {\n \"title\": \"What Is Google Gemma 4? Architecture, Benchmarks, and Why It ...\",\n \"url\": \"https://wavespeed.ai/blog/posts/what-is-google-gemma-4/\",\n \"description\": \"Google Gemma 4 is the most capable open model family from DeepMind yet, shipping four sizes under Apache 2.0 with multimodal input, native reasoning, and on-device deployment down to a Raspberry Pi. #gemma-4 #google #open-source #multimodal #on-device-ai. On April 2, 2026, Google DeepMind released **Gemma 4** — four open-weight models built from the same research behind Gemini 3, now available under the **Apache 2.0 license**. On LMArena’s text-only leaderboard, the 31B ranks #3 globally among open models with an ELO of ~1452. Every Gemma 4 model supports multimodal input out of the box:. * **Hugging Face**: `google/gemma-4-31B-it`, `google/gemma-4-26B-A4B-it`, `google/gemma-4-E4B-it`, `google/gemma-4-E2B-it`. Gemma 4 represents a serious move from Google in the open model space. The 31B dense model competes with models many times its size on reasoning and coding benchmarks. ### daVinci-MagiHuman: The Open-Source Model That Just Crushed Every Digital Human Generator### Introducing daVinci MagiHuman Image-to-Video on WaveSpeedAI### Introducing daVinci MagiHuman Text-to-Video on WaveSpeedAIIntroducing Google Lyria 3 Pro on WaveSpeedAI.\",\n \"position\": 1\n },\n {\n \"title\": \"Google's Gemma 4 is now available with Apache 2.0 licensing for ...\",\n \"url\": \"https://the-decoder.com/googles-gemma-4-is-now-available-with-apache-2-0-licensing-for-the-first-time/\",\n \"description\": \"**Google is releasing Gemma 4, its most capable open model family yet. Gemma 4 comes in four sizes: Effective 2B (E2B), Effective 4B (E4B), a 26B Mixture-of-Experts (MoE) model, and a 31B Dense model. The 31B model currently sits at 3rd place among all open models worldwide on the Arena AI Text Leaderboard, while the 26B MoE model ranks 6th. Google says Gemma 4 outperforms models 20 times its size. The 26B MoE model only activates 3.8 billion of its parameters during inference, which should make for especially fast token generation. Independent benchmarks from Artificial Analysis back up the numbers for the larger Gemma 4 models. According to Artificial Analysis, that's the second-best result among all open models with fewer than 40 billion parameters, just behind Qwen3.5 27B at 85.8 percent. Google AI Studio supports the 31B and 26B models, while Google AI Edge Gallery handles the E4B and E2B variants.\",\n \"position\": 2\n },\n {\n \"title\": \"Gemma 4 - How to Run Locally | Unsloth Documentation\",\n \"url\": \"https://unsloth.ai/docs/models/gemma-4\",\n \"description\": \"Run Google’s new Gemma 4 models locally, including E2B, E4B, 26B A4B, and 31B. Gemma 4 is Google DeepMind’s new family of open models, including **E2B**, **E4B**, **26B-A4B**, and **31B.** These multimodal, hybrid-thinking models support 140+ languages, up to **256K context**, and come in both dense and MoE variants. Gemma 4 can now be run and fine-tuned in Unsloth Studio, our new open-source web UI for local AI. export LLAMA_CACHE=\\\"unsloth/gemma-4-26B-A4B-GGUF\\\" export LLAMA_CACHE=\\\"unsloth/gemma-4-26B-A4B-GGUF\\\" ./llama.cpp/llama-cli \\\\ ./llama.cpp/llama-cli \\\\ -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_XL \\\\ -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_XL \\\\ --temp 1.0 \\\\ --temp 1.0 \\\\ --top-p 0.95 \\\\ --top-p 0.95 \\\\ --top-k 64 --top-k 64. hf download unsloth/gemma-4-26B-A4B-it-GGUF \\\\ hf download unsloth/gemma-4-26B-A4B-it-GGUF \\\\ --local-dir unsloth/gemma-4-26B-A4B-it-GGUF \\\\ --local-dir unsloth/gemma-4-26B-A4B-it-GGUF \\\\ --include \\\"*mmproj-BF16*\\\" \\\\ --include \\\"*mmproj-BF16*\\\" \\\\ --include \\\"*UD-Q4_K_XL*\\\" # Use \\\"*UD-Q2_K_XL*\\\" for Dynamic 2bit --include \\\"*UD-Q4_K_XL*\\\" # Use \\\"*UD-Q2_K_XL*\\\" for Dynamic 2bit. ./llama.cpp/llama-cli \\\\ ./llama.cpp/llama-cli \\\\ --model unsloth/gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf \\\\ --model unsloth/gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf \\\\ --mmproj unsloth/gemma-4-26B-A4B-it-GGUF/mmproj-BF16.gguf \\\\ --mmproj unsloth/gemma-4-26B-A4B-it-GGUF/mmproj-BF16.gguf \\\\ --temp 1.0 \\\\ --temp 1.0 \\\\ --top-p 0.95 \\\\ --top-p 0.95 \\\\ --top-k 64 --top-k 64.\",\n \"position\": 3\n },\n {\n \"title\": \"Google's Gemma 4 Arrives in Four Sizes With a License Developers ...\",\n \"url\": \"https://www.linkedin.com/pulse/googles-gemma-4-arrives-four-sizes-license-developers-henning-steier-kt0ke\",\n \"description\": \"The new Gemma models are available immediately in AI Studio (31B and 26B MoE) and AI Edge Gallery (E4B and E2B). Full model weights can be\",\n \"position\": 4\n },\n {\n \"title\": \"Gemma 4 - Models - Google DeepMind\",\n \"url\": \"https://deepmind.google/models/gemma/gemma-4/\",\n \"description\": \"[ Genie 3 Generate and explore interactive worlds](https://deepmind.google/models/genie/)[ Gemini Robotics Perceive, reason, use tools and interact](https://deepmind.google/models/gemini-robotics/). [ Gemma Build responsible AI applications at scale](https://deepmind.google/models/gemma/). [ SIMA 2 An agent that plays, reasons, and learns with you](https://deepmind.google/blog/sima-2-an-agent-that-plays-reasons-and-learns-with-you-in-virtual-3d-worlds/)[ Genie 3 Generate and explore interactive worlds](https://deepmind.google/models/genie/)[ AlphaGo Mastering the game of Go](https://deepmind.google/research/alphago/)[ Gemini Robotics Perceive, reason, use tools and interact](https://deepmind.google/models/gemini-robotics/). [ AlphaEarth Foundations Map our planet in unprecedented detail](https://deepmind.google/blog/alphaearth-foundations-helps-map-our-planet-in-unprecedented-detail/)[ WeatherNext Fast and accurate AI weather forecasting](https://deepmind.google/science/weathernext/)[ Weather Lab Test our experimental weather models](https://deepmind.google.com/science/weatherlab/?utm_source=deepmind.google&utm_medium=referral&utm_campaign=gdm&utm_content=). [ Genie 3 Generate and explore interactive worlds](https://deepmind.google/models/genie/)[ Gemini Robotics Perceive, reason, use tools and interact](https://deepmind.google/models/gemini-robotics/). [ Gemma Build responsible AI applications at scale](https://deepmind.google/models/gemma/). [ SIMA 2 An agent that plays, reasons, and learns with you](https://deepmind.google/blog/sima-2-an-agent-that-plays-reasons-and-learns-with-you-in-virtual-3d-worlds/)[ Genie 3 Generate and explore interactive worlds](https://deepmind.google/models/genie/)[ AlphaGo Mastering the game of Go](https://deepmind.google/research/alphago/)[ Gemini Robotics Perceive, reason, use tools and interact](https://deepmind.google/models/gemini-robotics/). [Try in Google AI Studio](https://aistudio.google.com/prompts/new_chat?model=gemma-4-31b-it&utm_source=deepmind.google&utm_medium=referral&utm_campaign=gdm&utm_content=).\",\n \"position\": 5\n }\n ]\n }\n}",
"tool_call_id": "call_849646efc5054fedad6ad1ec"
},
{
"role": "assistant",
"content": "Sí, salió el 2 de abril. Está buenísimo. Resumen:\n\n## Gemma 4 — Lo importante\n\nGoogle DeepMind lanzó **4 variantes**, y cambió la licencia a **Apache 2.0** (antes era restrictiva), ahora es *realmente* open source:\n\n| Modelo | Tipo | Parámetros activos | Contexto | Pensado para |\n|---|---|---|---|---|\n| **E2B** | Dense | 2.3B effective (5.1B total) | 128K | Celulares / edge |\n| **E4B** | Dense | 4.5B effective (8B total) | 128K | Laptops |\n| **26B A4B** | **MoE** | **3.8B activos** (25.2B total) | 256K | Workstation |\n| **31B** | Dense | 30.7B | 256K | Servidor / GPU |\n\nBenchmarks respetables: el 31B es **#3 abierto en LMSYS**, y el **26B MoE** corre a velocidad de modelo 4B porque solo activa 128 expertos (usa ~3.8B parámetros por token). Soporta texto, imagen, audio (E2B/E4B), reasoning mode nativo, function calling, y **140+ idiomas**.\n\n---\n\n## ¿Y en tu Oracle Always Free (4 ARM, 24 GB)?\n\nAcá es donde se pone interesante:\n\n### E2B / E4B → ✅ Sí, perfecto\nEn GGUF cuantizado (Q4_K_M o Q8):\n- **~2-4 GB de RAM**\n- **buena velocidad** en CPU ARM\n- ideales para chat, agente local, automatizaciones\n- son **nativamente multimodales** (texto + imagen + audio en E2B/E4B)\n\n### 26B A4B (MoE) → ✅ Es la verdadera joya\nAcá la clave: es **Mixture of Experts**, o sea que en inferencia **solo corre con 3.8B parámetros activos**, pero tiene la \"inteligencia\" de 26B guardados.\n\nEn GGUF Q4_K_M:\n- **~14-16 GB de RAM** (entran cómodos en 24 GB)\n- **rendimiento tipo 4B en velocidad** pero calidad mucho mayor\n- **256K contexto**\n- sería el **mejor modelo por lejos** que podés correr en Oracle Always Free\n\nEl único caveat: MoE en CPU puro va a ser más lento que un dense 4B en tokens/s, pero sigue siendo usable. Y en la práctica, la calidad del 26B A4B **debería aplastar** a cualquier 7B dense actual.\n\n### 31B Dense → 🟡 Posible pero justo\nEn Q4_K_M: **~17-19 GB de RAM**. Entra, pero deja menos margen para contexto. Y al ser dense, toda la computación pesa más. No me parece el mejor uso del hardware.\n\n---\n\n## Mi recomendación para tu Oracle Free\n\n**1. Gemma 4 E4B** — para uso general, rápido, estable\n**2. Gemma 4 26B A4B** — para calidad máxima con MoE, es la verdadera joya del release para tu hardware\n**3. Gemma 4 E2B** — como fallback ultra liviano\n\nYo pondría mi energía en el **26B A4B quantizado en llama.cpp**. Es el que más juego da: calidad de modelo grande con requerimientos que un CPU de 4 cores puede manejar razonablemente.\n\n¿Querés que lo instalemos en esta máquina para probarlo? Ya tenés el server ARM montado, le tiramos Ollama o llama.cpp y lo bajamos.",
"reasoning": "Now I have the full picture on Gemma 4. Let me compile a response about how it fits with Oracle Always Free."
},
{
"role": "user",
"content": "Nah solo de curioso. Por ahora seguiré usando providers."
},
{
"role": "assistant",
"content": "Perfecto, tiene todo el sentido. Los providers te dan modelos potentes sin tener que pelear con RAM y tok/s en CPU.\n\nAvísame cuando quieras meterle mano a algo, Chicho 👋",
"reasoning": "The user is saying they're just curious for now and will keep using API providers instead of self-hosting.\nThey're not asking for anything else to be done.\nI should acknowledge this briefly and move on.\nNo need for tools. Just a short, direct response per their preferences."
},
{
"role": "user",
"content": "Che estoy tratando de que hables en estr canal privado de discord pero no se si no te llegan mis msjs o si no funcionas en canales privados o no se https://discord.com/channels/1483873336718135336/1489503144290422914"
},
{
"role": "assistant",
"content": "Probablemente es eso: **yo no puedo “ver” ni leer canales privados de Discord por mi cuenta**, aunque me pases el link.\n\nSolo puedo responder en:\n- **este chat donde me estás escribiendo ahora**, o\n- algún canal/grupo **si la integración realmente me entrega los mensajes de ese lugar**\n\nO sea, el problema no es “canal privado sí/no” en abstracto, sino **si Hermes/Pipo está conectado a ese canal y recibe eventos de ahí**.\n\n## Lo importante\n- Ese link **no me da acceso**\n- Yo **no puedo abrir Discord internamente** ni revisar historial/canales\n- Si escribís en un canal privado y no me llega acá, entonces **esa conversación no está conectada a mi runtime**\n\n## Cómo probarlo rápido\nHacé una de estas:\n\n1. **Mandame un mensaje ahí mencionándome explícitamente**\n - algo tipo: `@Pipo decí hola`\n\n2. Si no responde:\n - revisá si el bot/integración **está agregado a ese canal privado**\n - revisá permisos:\n - ver canal\n - leer mensajes\n - enviar mensajes\n - leer historial\n - usar menciones / responder en threads si aplica\n\n3. Si querés aislar si el problema es el canal:\n - probá en **otro canal donde sabés que sí tiene acceso**\n - o mandame desde ese privado algo muy simple y fijate si aparece\n\n## Mi sospecha\nLo más probable es:\n- **el bot no fue agregado a ese canal privado**, o\n- **no tiene permisos para verlo/responder**, o\n- esa superficie de Discord **no está conectada** a Hermes\n\nSi querés, te ayudo a diagnosticarlo paso a paso y armamos una checklist corta de permisos/config para ese canal privado.",
"reasoning": "**Clarifying limitations**\n\nI need to provide a direct answer to the user about their question regarding a private channel. I should mention that I can't access Discord’s specific APIs or search through channel history, so I can only read messages sent directly to me. It's also important to clarify that just providing a link doesn't grant me access. Maybe I could suggest the user test it by trying to mention or invite the bot/app in their DM. I should be careful in how I phrase this!",
"finish_reason": "stop",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_06277abd1b879d230169cf578049e881988e81e786c67d10d3",
"summary": [
{
"type": "summary_text",
"text": "**Clarifying limitations**\n\nI need to provide a direct answer to the user about their question regarding a private channel. I should mention that I can't access Discord’s specific APIs or search through channel history, so I can only read messages sent directly to me. It's also important to clarify that just providing a link doesn't grant me access. Maybe I could suggest the user test it by trying to mention or invite the bot/app in their DM. I should be careful in how I phrase this!"
}
]
}
]
}
]
}