Skill: twinsgeeks/deepseek-deepseek-coder (version 1.0.3)

--- name: deepseek-deepseek-coder description: DeepSeek DeepSeek-Coder — run DeepSeek-V3, DeepSeek-R1, DeepSeek-Coder across your local fleet. 7-signal scoring routes every request to the best device. Cross-platform (macOS, Linux, Windows). Zero cloud costs via Ollama Herd. version: 1.0.3 homepage: https://github.com/geeks-accelerator/ollama-herd metadata: {"openclaw":{"emoji":"brain","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["darwin","linux","windows"]}} --- # DeepSeek — Run DeepSeek Models Across Your Local Fleet Run DeepSeek-V3, DeepSeek-R1, and DeepSeek-Coder on your own hardware. The fleet router picks the best device for every request — no cloud API needed, zero per-token costs, all data stays on your machines. ## Supported DeepSeek models | Model | Parameters | Ollama name | Best for | |-------|-----------|-------------|----------| | **DeepSeek-V3** | 671B MoE (37B active) | `deepseek-v3` | General — matches GPT-4o on most benchmarks | | **DeepSeek-V3.1** | 671B MoE | `deepseek-v3.1` | Hybrid thinking/non-thinking modes | | **DeepSeek-V3.2** | 671B MoE | `deepseek-v3.2` | Improved reasoning + agent performance | | **DeepSeek-R1** | 1.5B–671B | `deepseek-r1` | Reasoning — approaches O3 and Gemini 2.5 Pro | | **DeepSeek-Coder** | 1.3B–33B | `deepseek-coder` | Code generation (87% code, 13% NL training) | | **DeepSeek-Coder-V2** | 236B MoE (21B active) | `deepseek-coder-v2` | Code — matches GPT-4 Turbo on code tasks | ## Setup ```bash pip install ollama-herd herd # start the router (port 11435) herd-node # run on each machine ``` Package: [ollama-herd](https://pypi.org/project/ollama-herd/) | Repo: [github.com/geeks-accelerator/ollama-herd](https://github.com/geeks-accelerator/ollama-herd) Models are pulled on demand — the router auto-pulls when a request arrives for a model not yet on any node, or you can pull manually via the dashboard. No models are downloaded during installation. ## Use DeepSeek through the fleet ### OpenAI SDK ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:11435/v1", [REDACTED_SECRET]") # DeepSeek-R1 for reasoning response = client.chat.completions.create( model="deepseek-r1:70b", messages=[{"role": "user", "content": "Prove that there are infinitely many primes"}], stream=True, ) for chunk in response: print(chunk.choices[0].delta.content or "", end="") ``` ### DeepSeek-Coder for code ```python response = client.chat.completions.create( model="deepseek-coder-v2:16b", messages=[{"role": "user", "content": "Write a Redis cache decorator in Python"}], ) print(response.choices[0].message.content) ``` ### Ollama API ```bash # DeepSeek-V3 general chat curl http://localhost:11435/api/chat -d '{ "model": "deepseek-v3", "messages": [{"role": "user", "content": "Explain quantum computing"}], "stream": false }' # DeepSeek-R1 reasoning curl http://localhost:11435/api/chat -d '{ "model": "deepseek-r1:70b", "messages": [{"role": "user", "content": "Solve this step by step: ..."}], "stream": false }' ``` ## Hardware recommendations (optional — choose models that fit your RAM) > **Cross-platform:** These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works. The fleet router runs on all platforms. DeepSeek offers models at every size. Pick the one that fits your available memory — smaller models work great for most tasks: | Model | Min RAM | Recommended hardware | |-------|---------|---------------------| | `deepseek-r1:1.5b` | 4GB | Any Mac | | `deepseek-r1:7b` | 8GB | Mac Mini M4 (16GB) | | `deepseek-r1:14b` | 12GB | Mac Mini M4 (24GB) | | `deepseek-r1:32b` | 24GB | Mac Mini M4 Pro (48GB) | | `deepseek-r1:70b` | 48GB | Mac Studio M4 Max (128GB) | | `deepseek-coder-v2:16b` | 12GB | Mac Mini M4 (24GB) | | `deepseek-v3` | 256GB+ | Mac Studio M3 Ultra (512GB) | The fleet router automatically sends requests to the machine where the model is loaded — no manual routing needed. ## Why run DeepSeek locally - **Zero cost** — DeepSeek API charges per token. Local is free after hardware. - **Privacy** — code and business data never leave your network. - **No rate limits** — DeepSeek API throttles during peak hours. Local has no throttle. - **Availability** — DeepSeek API has had outages. Your hardware doesn't depend on their servers. - **Fleet routing** — multiple machines share the load. One busy? Request goes to the next. ## Fleet features - **7-signal scoring** — picks the optimal node for every request - **Auto-retry** — fails over to next best node transparently - **VRAM-aware fallback** — routes to a loaded model in the same category instead of cold-loading - **Context protection** — prevents expensive model reloads from `num_ctx` changes - **Request tagging** — track per-project DeepSeek usage ## Also available on this fleet ### Other LLM models Llama 3.3, Qwen 3.5, Phi 4, Mistral, Gemma 3 — any Ollama model routes through the same endpoint. ### Image generation ```bash curl -o image.png http://localhost:11435/api/generate-image \ -H "Content-Type: application/json" \ -d '{"model":"z-image-turbo","prompt":"a sunset","width":1024,"height":1024,"steps":4}' ``` ### Speech-to-text ```bash curl http://localhost:11435/api/transcribe -F "audio=@recording.wav" ``` ### Embeddings ```bash curl http://localhost:11435/api/embeddings -d '{"model":"nomic-embed-text","prompt":"query"}' ``` ## Dashboard `http://localhost:11435/dashboard` — monitor DeepSeek requests alongside all other models. Per-model latency, token throughput, health checks. ## Full documentation [Agent Setup Guide](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/agent-setup-guide.md) ## Guardrails - **Model downloads require explicit user confirmation** — DeepSeek models range from 1GB (1.5B) to 400GB+ (671B). Always confirm before pulling. - **Model deletion requires explicit user confirmation** — never remove models without asking. - Never delete or modify files in `~/.fleet-manager/`. - If a DeepSeek model is too large for available memory, suggest a smaller variant (e.g., `deepseek-r1:7b` instead of `:70b`). - No models are downloaded automatically — all pulls are user-initiated or require opt-in via the `auto_pull` setting.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)