Instructions to use nbeerbower/Hemlock2-Coder-7B-GRPO 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 nbeerbower/Hemlock2-Coder-7B-GRPO 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 nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0 # Run inference directly in the terminal: llama cli -hf nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0 # Run inference directly in the terminal: llama cli -hf nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_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 nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_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 nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0
Use Docker
docker model run hf.co/nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0
- LM Studio
- Jan
- Ollama
How to use nbeerbower/Hemlock2-Coder-7B-GRPO with Ollama:
ollama run hf.co/nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0
- Unsloth Studio
How to use nbeerbower/Hemlock2-Coder-7B-GRPO with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for nbeerbower/Hemlock2-Coder-7B-GRPO to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for nbeerbower/Hemlock2-Coder-7B-GRPO to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for nbeerbower/Hemlock2-Coder-7B-GRPO to start chatting
- Pi
How to use nbeerbower/Hemlock2-Coder-7B-GRPO with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use nbeerbower/Hemlock2-Coder-7B-GRPO with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_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 nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use nbeerbower/Hemlock2-Coder-7B-GRPO with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_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 "nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_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"
- Docker Model Runner
How to use nbeerbower/Hemlock2-Coder-7B-GRPO with Docker Model Runner:
docker model run hf.co/nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0
- Lemonade
How to use nbeerbower/Hemlock2-Coder-7B-GRPO with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nbeerbower/Hemlock2-Coder-7B-GRPO:Q8_0
Run and chat with the model
lemonade run user.Hemlock2-Coder-7B-GRPO-Q8_0
List all available models
lemonade list
File size: 1,579 Bytes
03c5b1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ---
license: apache-2.0
base_model: hemlang/Hemlock2-Coder-7B
datasets:
- hemlang/hemlock-codex3-SFT
tags:
- hemlock
- code
- grpo
- reinforcement-learning
---
# Hemlock2-Coder-7B-GRPO
[hemlang/Hemlock2-Coder-7B](https://huggingface.co/hemlang/Hemlock2-Coder-7B) improved with
execution-reward GRPO ([grimoire](https://github.com/Schneewolf-Labs/grimoire) ≥ 2.0 /
[hemlock-rl](https://github.com/Schneewolf-Labs/hemlock-rl)). Completions were executed in the
Hemlock interpreter's sandbox and rewarded for exactly reproducing verified reference stdout.
Strictly improves the base model on [hembench](https://github.com/hemlang/hemlock/tree/main/benchmark)
(zero-shot, n=5, benchmark-overlapping training tasks held out):
| | pass@1 | pass@5 |
|---|---|---|
| Hemlock2-Coder-7B | 28.9% | 55.3% |
| **Hemlock2-Coder-7B-GRPO** | **36.8%** (+7.9) | **57.9%** (+2.6) |
Largest gains in syntax (L1 pass@1 2/9 → 4/9, pass@5 7/9 → 8/9) and systems/concurrency
(L4 pass@1 1/7 → 3/7).
## Training
- **Method**: GRPO (LoRA r=16 α=32, β=0.1 KL vs frozen base, G=8, dynamic sampling,
unscaled advantages), 186 deduplicated prompts × 3 epochs.
- **Rewards**: sandboxed execution; exact stdout match vs verified reference = 2.0,
runs-but-wrong = 0.5, error = −0.5, no code / timeout = −1.0.
- **Data**: execution-verified translation/generation tasks (now published as
[hemlang/hemlock-codex3-SFT](https://huggingface.co/datasets/hemlang/hemlock-codex3-SFT)),
one language-variant per task, hembench-name-overlapping tasks excluded.
Q8_0 GGUF included.
|