Instructions to use Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-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 Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-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 Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M # Run inference directly in the terminal: llama cli -hf Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M # Run inference directly in the terminal: llama cli -hf Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
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 Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
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 Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
Use Docker
docker model run hf.co/Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO with Ollama:
ollama run hf.co/Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
- Unsloth Desktop
- Pi
How to use Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
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": "Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO with Docker Model Runner:
docker model run hf.co/Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
- Lemonade
How to use Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
Run and chat with the model
lemonade run user.Hugston-yarin-shakedQwen3-Codeforces-GRPO-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-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 Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
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 Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M
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 "Trilogix1/Hugston-yarin-shakedQwen3-Codeforces-GRPO:Q4_K_M" \ --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"
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
- This model was converted and Quantized by Hugston Team.
- Tested.
- Watch HugstonOne coding and preview in action: https://vimeo.com/1121493834?share=copy&fl=sv&fe=ci Usage -Download App HugstonOne at Hugston.com or at https://github.com/Mainframework -Download model from https://hugston.com/explore?folder=llm_models or Huggingface -If you already have the Llm Model downloaded chose it by clicking pick model in HugstonOne -Then click Load model in Cli or Server
- -For multimodal use you need a VL/multimodal LLM model with the Mmproj file in the same folder. -Select model and select mmproj.
Model finetune origin: https://huggingface.co/yarin-shaked/Qwen3-Codeforces-GRPO
Model Card for Hugston-Qwen3-Codeforces-GRPO This model is a fine-tuned version of Qwen/Qwen3-0.6B on the open-r1/codeforces dataset. It has been trained using TRL.
This model was converted and Quantized by Hugston Team.
You can use the model with HugstonOne Enterprise Edition
Tested.
Watch HugstonOne coding and preview in action: https://vimeo.com/1121493834?share=copy&fl=sv&fe=ci Usage -Download App HugstonOne at Hugston.com or at https://github.com/Mainframework -Download model from https://hugston.com/explore?folder=llm_models or Huggingface -If you already have the Llm Model downloaded chose it by clicking pick model in HugstonOne -Then click Load model in Cli or Server
-For multimodal use you need a VL/multimodal LLM model with the Mmproj file in the same folder. -Select model and select mmproj.
-Note: if the mmproj is inside the same folder with other models non multimodal, the non model will not load unless the mmproj is moved from folder.
Training procedure Visualize in Weights & Biases
This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
Framework versions TRL: 0.18.0 Transformers: 4.52.3 Pytorch: 2.6.0 Datasets: 4.2.0 Tokenizers: 0.21.4 Citations Cite GRPO as:
@article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } Cite TRL as:
@misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} }
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