Instructions to use smalinin/DeepSeek-V4.1-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use smalinin/DeepSeek-V4.1-Flash-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS # Run inference directly in the terminal: llama cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS # Run inference directly in the terminal: llama cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS # Run inference directly in the terminal: ./llama-cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS # Run inference directly in the terminal: ./build/bin/llama-cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
Use Docker
docker model run hf.co/smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
- LM Studio
- Jan
- vLLM
How to use smalinin/DeepSeek-V4.1-Flash-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "smalinin/DeepSeek-V4.1-Flash-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "smalinin/DeepSeek-V4.1-Flash-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
- Ollama
How to use smalinin/DeepSeek-V4.1-Flash-GGUF with Ollama:
ollama run hf.co/smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
- Unsloth Desktop
- Pi
How to use smalinin/DeepSeek-V4.1-Flash-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use smalinin/DeepSeek-V4.1-Flash-GGUF with Docker Model Runner:
docker model run hf.co/smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
- Lemonade
How to use smalinin/DeepSeek-V4.1-Flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
Run and chat with the model
lemonade run user.DeepSeek-V4.1-Flash-GGUF-IQ3_XS
List all available models
lemonade list
- Hermes Agent
How to use smalinin/DeepSeek-V4.1-Flash-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use smalinin/DeepSeek-V4.1-Flash-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "smalinin/DeepSeek-V4.1-Flash-GGUF:IQ3_XS" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Upload Q2_K/README.md with huggingface_hub
Browse files- Q2_K/README.md +16 -0
Q2_K/README.md
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# DeepSeek-V4.1 complete Q2 quality overlay
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This seven-shard set preserves the repaired DeepSeek41 Engram metadata and the four official
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BF16 `engram_q/k` tensors from `artifacts/quality`, and additionally restores all 80 text-model
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`hc_attn_fn/hc_ffn_fn` projections to their official F32 payloads.
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The physical GGUF files are stored under
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`/media/sergei/WW2T/models/vcruz305/DeepSeek-V4.1-Flash-Q2_K-quality-f32-mhc/`.
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The shard entries in this directory are symbolic links. Original community files and the earlier
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quality overlay were not modified.
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Stage 14 accepted this set as the recommended Q2 quality artifact. On the paired 32-chunk
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Wikitext-2 gate it reached PPL 6.2046 versus 6.3085 for the earlier Q2-mHC overlay.
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Provenance, exact HTTP ranges, LFS hashes, rewrite manifests and final checksums are under
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`validation/stage14/`.
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