Instructions to use megalodon-ml/snapfill-llm-ft 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 megalodon-ml/snapfill-llm-ft 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 megalodon-ml/snapfill-llm-ft:Q4_0 # Run inference directly in the terminal: llama cli -hf megalodon-ml/snapfill-llm-ft:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf megalodon-ml/snapfill-llm-ft:Q4_0 # Run inference directly in the terminal: llama cli -hf megalodon-ml/snapfill-llm-ft:Q4_0
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 megalodon-ml/snapfill-llm-ft:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf megalodon-ml/snapfill-llm-ft:Q4_0
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 megalodon-ml/snapfill-llm-ft:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf megalodon-ml/snapfill-llm-ft:Q4_0
Use Docker
docker model run hf.co/megalodon-ml/snapfill-llm-ft:Q4_0
- LM Studio
- Jan
- Ollama
How to use megalodon-ml/snapfill-llm-ft with Ollama:
ollama run hf.co/megalodon-ml/snapfill-llm-ft:Q4_0
- Unsloth Desktop
- Pi
How to use megalodon-ml/snapfill-llm-ft with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf megalodon-ml/snapfill-llm-ft:Q4_0
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": "megalodon-ml/snapfill-llm-ft:Q4_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use megalodon-ml/snapfill-llm-ft with Docker Model Runner:
docker model run hf.co/megalodon-ml/snapfill-llm-ft:Q4_0
- Lemonade
How to use megalodon-ml/snapfill-llm-ft with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull megalodon-ml/snapfill-llm-ft:Q4_0
Run and chat with the model
lemonade run user.snapfill-llm-ft-Q4_0
List all available models
lemonade list
- Hermes Agent
How to use megalodon-ml/snapfill-llm-ft with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf megalodon-ml/snapfill-llm-ft:Q4_0
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 megalodon-ml/snapfill-llm-ft:Q4_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use megalodon-ml/snapfill-llm-ft with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf megalodon-ml/snapfill-llm-ft:Q4_0
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 "megalodon-ml/snapfill-llm-ft:Q4_0" \ --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"
SnapFill stage-2 fine-tunes (Team 022, internal)
Two phone-ready GGUFs for the form-filling LLM (stage 2). Both are LoRA fine-tunes on the v3 compact prompt, merged and quantized at the llama.cpp commit GenieX 0.8.0 pins (94256114c).
| File | Base | Quant | Size | Field accuracy, Gemini eval / Vinay (turing, grammar on) |
|---|---|---|---|---|
gemma-v3-qat-q4_0.gguf |
google/gemma-4-E2B-it-qat-q4_0-unquantized |
Q4_0, trained with Q4_0 fake-quant (QAT) | 3.35 GB | 0.915 / 0.801 (correct values 0.91 / 0.91, catches 10/10 off-topic) |
q17-v3-q4_0im.gguf |
unsloth/Qwen3-1.7B |
Q4_0 with imatrix calibration | 1.06 GB | 0.782 / 0.830 (correct values 0.82 / 0.93) |
How to use them:
- Send the prompt exactly as
docs/stage2-prompt-format.mdin the team repo specifies:- compact messages only (system + user, no few-shots);
PromptBuilder(fewshot = false), i.e.ModelDescriptor.fineTuned = true;- the v3 grammar from
ToolGrammar; topK = 1, thinking off through the chat template;nCtx = 2048.
- Any other prompt text silently lowers accuracy. Do not add
/no_think. - Keep
gemma-v3-qatas Q4_0. Its f16/Q8_0 exports score 0.25: the adapter only works after Q4_0 rounding. - Scores come from Gemini-written eval cases plus Vinay's 72 human-written cases (
eval/llm/README.md). They are not yet checked on real speech.
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