Image-Text-to-Text
GGUF
deepseek
deepseek-v4.1
mixture-of-experts
llama.cpp
vision
imatrix
conversational
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:Q2_K_DOWN # Run inference directly in the terminal: llama cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:Q2_K_DOWN
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:Q2_K_DOWN # Run inference directly in the terminal: llama cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:Q2_K_DOWN
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:Q2_K_DOWN # Run inference directly in the terminal: ./llama-cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:Q2_K_DOWN
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:Q2_K_DOWN # Run inference directly in the terminal: ./build/bin/llama-cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:Q2_K_DOWN
Use Docker
docker model run hf.co/smalinin/DeepSeek-V4.1-Flash-GGUF:Q2_K_DOWN
- 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": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/smalinin/DeepSeek-V4.1-Flash-GGUF:Q2_K_DOWN
- Ollama
How to use smalinin/DeepSeek-V4.1-Flash-GGUF with Ollama:
ollama run hf.co/smalinin/DeepSeek-V4.1-Flash-GGUF:Q2_K_DOWN
- 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:Q2_K_DOWN
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:Q2_K_DOWN" } ] } } }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:Q2_K_DOWN
- 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:Q2_K_DOWN
Run and chat with the model
lemonade run user.DeepSeek-V4.1-Flash-GGUF-Q2_K_DOWN
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:Q2_K_DOWN
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:Q2_K_DOWN
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:Q2_K_DOWN
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:Q2_K_DOWN" \ --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"
File size: 845 Bytes
a8ff93b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # DeepSeek-V4.1 complete Q2 quality overlay
This seven-shard set preserves the repaired DeepSeek41 Engram metadata and the four official
BF16 `engram_q/k` tensors from `artifacts/quality`, and additionally restores all 80 text-model
`hc_attn_fn/hc_ffn_fn` projections to their official F32 payloads.
The physical GGUF files are stored under
`/media/sergei/WW2T/models/vcruz305/DeepSeek-V4.1-Flash-Q2_K-quality-f32-mhc/`.
The shard entries in this directory are symbolic links. Original community files and the earlier
quality overlay were not modified.
Stage 14 accepted this set as the recommended Q2 quality artifact. On the paired 32-chunk
Wikitext-2 gate it reached PPL 6.2046 versus 6.3085 for the earlier Q2-mHC overlay.
Provenance, exact HTTP ranges, LFS hashes, rewrite manifests and final checksums are under
`validation/stage14/`.
|