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:Q8_0_ENGRAM # Run inference directly in the terminal: llama cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:Q8_0_ENGRAM
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:Q8_0_ENGRAM # Run inference directly in the terminal: llama cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:Q8_0_ENGRAM
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:Q8_0_ENGRAM # Run inference directly in the terminal: ./llama-cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:Q8_0_ENGRAM
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:Q8_0_ENGRAM # Run inference directly in the terminal: ./build/bin/llama-cli -hf smalinin/DeepSeek-V4.1-Flash-GGUF:Q8_0_ENGRAM
Use Docker
docker model run hf.co/smalinin/DeepSeek-V4.1-Flash-GGUF:Q8_0_ENGRAM
- 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:Q8_0_ENGRAM
- Ollama
How to use smalinin/DeepSeek-V4.1-Flash-GGUF with Ollama:
ollama run hf.co/smalinin/DeepSeek-V4.1-Flash-GGUF:Q8_0_ENGRAM
- 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:Q8_0_ENGRAM
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:Q8_0_ENGRAM" } ] } } }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:Q8_0_ENGRAM
- 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:Q8_0_ENGRAM
Run and chat with the model
lemonade run user.DeepSeek-V4.1-Flash-GGUF-Q8_0_ENGRAM
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:Q8_0_ENGRAM
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:Q8_0_ENGRAM
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:Q8_0_ENGRAM
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:Q8_0_ENGRAM" \ --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"
Update README.md
Browse files
README.md
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- deepseek-v4.1
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- llama.cpp
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quantized_by: vcruz305
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---
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# DeepSeek-V4.1-Flash GGUF
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## Recipe
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How to build the engine, serve it, and the gotchas, plus the current status:
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## Status
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**These files do not run on upstream llama.cpp yet.** Conversion works and is open as
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[ggml-org/llama.cpp#28696](https://github.com/ggml-org/llama.cpp/pull/28696). The runtime is in
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progress on the `runtime/deepseek41` branch of
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[vcruz305/llama.cpp](https://github.com/vcruz305/llama.cpp): the loader, the engram tables and the
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hyper-connections work and are verified against the reference implementation, and the sparse
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attention is the remaining piece.
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`general.architecture = deepseek4` and is being redone as `deepseek41`.
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The architecture string is `deepseek41`, following llama.cpp's habit of dropping the `_v`
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(`deepseek_v2` became `deepseek2`, `deepseek_v3.2` became `deepseek32`).
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**
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`layer_ids`) were written with a hardcoded `deepseek4.engram.*` prefix instead of resolving
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`{arch}.engram.*` like every other arch-scoped key in the file. `general.architecture` and all
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38 other arch-scoped keys were already correct (`deepseek41.*`); only these 4 were wrong, which
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would have made the `runtime/deepseek41` loader fail to find Engram config on an otherwise
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loadable file. Fixed in place via a KV-only rewrite (tensor data untouched, verified
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byte-identical by SHA-256) on all five quant rungs' first shard, where GGUF split files store
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metadata. Confirmed live: all five now read `deepseek41.engram.*`.
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## Files
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Ladder in order: **Q2_K, Q3_K_M, Q4_K_M**. Measured tensor payload:
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| File | Quant | Bytes | GiB |
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| `DeepSeek-V4.1-Flash-Q2_K.gguf` | Q2_K | 264,514,761,248 | 246.3 |
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| `DeepSeek-V4.1-Flash-Q3_K_M.gguf` | Q3_K_M | 347,270,954,112 | 323.4 |
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| `DeepSeek-V4.1-Flash-Q4_K_M.gguf` | Q4_K_M | pending | |
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Split into parts, since each exceeds the Hub's single file limit.
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Q5_K_M is skipped unless asked for. The routed experts arrive as MXFP4 at 4.25 bpw, so higher rungs
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move parts of the mixture *up* rather than down: Q3_K_M already lands at 0.684 of the Q8_0 staging
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file, and Q5_K_M would be close enough to Q8_0 to be poor value.
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Most of the file is the two engram tables, roughly 196.6B parameters between them. They follow the
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rung, 99,611 to 40,284 MiB each between q8_0 and q3_K.
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Apache/MIT from upstream.
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- deepseek-v4.1
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- llama.cpp
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quantized_by: vcruz305
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quality recovered: smalinin
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---
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# DeepSeek-V4.1-Flash GGUF
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## Recipe
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How to build the engine, serve it, and the gotchas, plus the current status:
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[https://github.com/smalinin/llama.cpp/tree/my_build_deepseek41](https://github.com/smalinin/llama.cpp/tree/my_build_deepseek41)
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```
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./llama-server \
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--model ./DeepSeek-V4.1-Flash-Q2_K-00001-of-00007.gguf \
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--host 127.0.0.1 \
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--port 8080 \
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--ctx-size 64000 \
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--batch-size 2048 \
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--ubatch-size 256 \
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--parallel 1 \
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--n-gpu-layers auto \
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--fit on \
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--fit-ctx 64000 \
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--fit-target 2048 \
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--load-mode mmap \
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--lazy-mode auto \
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--flash-attn on \
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--no-warmup \
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--no-context-shift \
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--jinja \
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--chat-template-file ./models/templates/deepseek-ai-DeepSeek-V4.1.jinja \
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--chat-template-kwargs '{"reasoning_effort":80,"enable_thinking":true}' \
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--reasoning-format deepseek \
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--no-reasoning-preserve \
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--no-prefill-assistant
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```
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## Status
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**These files do not run on upstream llama.cpp yet.**
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## Files
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Apache/MIT from upstream.
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