mistral-codestral

# Mistral & Codestral — Mistral AI Models on Your Local Fleet Mistral AI's open-source models run locally on your hardware. Mistral Large for frontier reasoning, Mistral-Nemo for efficiency, Codestral for code generation. The fleet router picks the best device for every Mistral request. ## Supported Mistral models | Mistral Model | Parameters | Ollama name | Best for | |---------------|-----------|-------------|----------| | **Codestral** (by Mistral) | 22B | `codestral` | Mistral's code specialist — 80+ languages | | **Mistral Large** | 123B | `mistral-large` | Mistral's frontier reasoning, multilingual | | **Mistral-Nemo** | 12B | `mistral-nemo` | Mistral's efficient general-purpose model | | **Mistral-Small** | 22B | `mistral-small` | Mistral's fast reasoning model | | **Mistral 7B** | 7B | `mistral:7b` | Mistral's lightweight model | ## Setup Mistral locally ```bash pip install ollama-herd # install Mistral fleet router herd # start the Mistral-compatible router herd-node # run on each device — Mistral requests route automatically ``` No Mistral models downloaded during installation. All Mistral model pulls are user-initiated. ## Codestral code generation Codestral is Mistral AI's dedicated coding model — trained on 80+ programming languages with fill-in-the-middle support. ```python from openai import OpenAI # Connect to local Mistral fleet mistral_fleet = OpenAI(base_url="http://localhost:11435/v1", api_key="not-needed") # Codestral by Mistral for code generation codestral_response = mistral_fleet.chat.completions.create( model="codestral", # Mistral's Codestral model messages=[{"role": "user", "content": "Write a Redis-backed rate limiter in Go"}], ) print(codestral_response.choices[0].message.content) ``` ### Codestral via curl ```bash # Codestral code generation on local Mistral fleet curl http://localhost:11435/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model": "codestral", "messages": [{"role": "user", "content": "Implement a B-tree in Rust — Mistral Codestral excels at systems programming"}]}' ``` ## Mistral Large reasoning ```bash # Mistral Large for complex reasoning curl http://localhost:11435/api/chat -d '{ "model": "mistral-large", "messages": [{"role": "user", "content": "Compare Mistral vs GPT-4 for enterprise deployments"}], "stream": false }' ``` ### Mistral-Nemo for efficiency ```bash # Mistral-Nemo — best quality/size ratio from Mistral AI curl http://localhost:11435/api/chat -d '{ "model": "mistral-nemo", "messages": [{"role": "user", "content": "Summarize this Mistral AI technical paper"}], "stream": false }' ``` ## Mistral hardware recommendations > **Cross-platform:** These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works. The fleet router runs on all platforms. | Mistral Model | Min RAM | Example hardware | |---------------|---------|------------------------| | `mistral:7b` | 8GB | Any Mac — lightweight Mistral | | `mistral-nemo` | 10GB | Mac Mini (16GB) — efficient Mistral | | `codestral` | 16GB | Mac Mini (24GB) — Mistral's code model | | `mistral-small` | 16GB | Mac Mini (24GB) — fast Mistral | | `mistral-large` | 80GB | Mac Studio (128GB) — Mistral's best | ## Monitor Mistral fleet ```bash # See which Mistral models are loaded curl -s http://localhost:11435/api/ps | python3 -m json.tool # Mistral fleet overview curl -s http://localhost:11435/fleet/status | python3 -m json.tool # Mistral model performance stats curl -s http://localhost:11435/dashboard/api/models | python3 -m json.tool ``` Example Mistral fleet response: ```json { "node_id": "Mistral-Server", "models_loaded": ["codestral:22b", "mistral-nemo:12b"], "mistral_inference": "active" } ``` Mistral dashboard at `http://localhost:11435/dashboard`. ## Also available alongside Mistral ### Other LLMs (same Mistral-compatible endpoint) Llama 3.3, Qwen 3.5, DeepSeek-V3, Phi 4, Gemma 3 — route alongside Mistral models. ### Image generation ```bash curl http://localhost:11435/api/generate-image \ -d '{"model": "z-image-turbo", "prompt": "Mistral AI logo reimagined as abstract art", "width": 512, "height": 512}' ``` ### Speech-to-text ```bash curl http://localhost:11435/api/transcribe -F "file=@mistral_meeting.wav" -F "model=qwen3-asr" ``` ### Embeddings ```bash curl http://localhost:11435/api/embed \ -d '{"model": "nomic-embed-text", "input": "Mistral AI open source language models Codestral"}' ``` ## Full documentation - [Agent Setup Guide](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/agent-setup-guide.md) - [API Reference](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/api-reference.md) ## Contribute Ollama Herd is open source (MIT). Run Mistral locally, contribute globally: - [Star on GitHub](https://github.com/geeks-accelerator/ollama-herd) — help Mistral users find local inference - [Open an issue](https://github.com/geeks-accelerator/ollama-herd/issues) — share your Mistral setup - **PRs welcome** — `CLAUDE.md` gives AI agents full context. 444 tests. ## Guardrails - **Mistral model downloads require explicit user confirmation** — Mistral models range from 4GB to 70GB+. - **Mistral model deletion requires explicit user confirmation.** - Never delete or modify files in `~/.fleet-manager/`. - No Mistral models downloaded automatically — all pulls are user-initiated.