Skill: twinsgeeks/deepseek-deepseek-v3 (version 1.0.1)

--- name: deepseek description: DeepSeek models on your local fleet — DeepSeek-V3, DeepSeek-V3.2, DeepSeek-R1, DeepSeek-Coder routed across multiple devices via Ollama Herd. 7-signal scoring picks the best machine for every request. Run DeepSeek locally on Apple Silicon with zero cloud costs. version: 1.0.0 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"]}} --- # 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 # Pull a DeepSeek model ollama pull deepseek-r1:70b ``` Package: [ollama-herd](https://pypi.org/project/ollama-herd/) | Repo: [github.com/geeks-accelerator/ollama-herd](https://github.com/geeks-accelerator/ollama-herd) ## 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 DeepSeek models are large. Here's what fits where: | 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 - Never pull or delete DeepSeek models without user confirmation — downloads are 4-400+ GB. - Never delete or modify files in `~/.fleet-manager/`. - If a DeepSeek model is too large for available memory, suggest a smaller variant.


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