Text Generation
Transformers
English
reinforcement-learning
grpo
openenv
blast-radius
qwen2.5
unsloth
sft
rl-environment
Instructions to use Idred/BlastRadius-GRPO-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Idred/BlastRadius-GRPO-Checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Idred/BlastRadius-GRPO-Checkpoints")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Idred/BlastRadius-GRPO-Checkpoints", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Idred/BlastRadius-GRPO-Checkpoints with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Idred/BlastRadius-GRPO-Checkpoints" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Idred/BlastRadius-GRPO-Checkpoints", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Idred/BlastRadius-GRPO-Checkpoints
- SGLang
How to use Idred/BlastRadius-GRPO-Checkpoints with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Idred/BlastRadius-GRPO-Checkpoints" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Idred/BlastRadius-GRPO-Checkpoints", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Idred/BlastRadius-GRPO-Checkpoints" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Idred/BlastRadius-GRPO-Checkpoints", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use Idred/BlastRadius-GRPO-Checkpoints 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 Idred/BlastRadius-GRPO-Checkpoints 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 Idred/BlastRadius-GRPO-Checkpoints to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Idred/BlastRadius-GRPO-Checkpoints to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Idred/BlastRadius-GRPO-Checkpoints", max_seq_length=2048, ) - Docker Model Runner
How to use Idred/BlastRadius-GRPO-Checkpoints with Docker Model Runner:
docker model run hf.co/Idred/BlastRadius-GRPO-Checkpoints
| license: apache-2.0 | |
| language: | |
| - en | |
| base_model: unsloth/Qwen2.5-14B-Instruct-bnb-4bit | |
| pipeline_tag: text-generation | |
| library_name: transformers | |
| tags: | |
| - reinforcement-learning | |
| - grpo | |
| - openenv | |
| - blast-radius | |
| - qwen2.5 | |
| - unsloth | |
| - sft | |
| - rl-environment | |
| metrics: | |
| - type: reward | |
| value: 0.72 | |
| name: Mean Episode Reward | |
| - type: reward | |
| value: 0.75 | |
| name: Format Reward | |
| - type: reward | |
| value: 0.48 | |
| name: KL Divergence | |
| # BlastRadius — GRPO Model Checkpoints | |
| This repository contains the trained model checkpoints. | |
| ## Live Demo | |
| https://huggingface.co/spaces/Idred/BlastRadius-OpenEnv | |
| ## Training Notebook | |
| https://huggingface.co/spaces/Idred/BlastRadius-OpenEnv/blob/main/BlastRadius_A100_Training_v2.ipynb | |
| ## Training Details | |
| - **Hardware:** Hugging Face Jobs (H200 GPU) | |
| - **Framework:** PyTorch 2.6 (CUDA 12.4) | |
| - **Approach:** SFT + GRPO (Reinforcement Learning) | |
| - **Experiment Tracking:** Weights & Biases (WandB) | |
| ## Note | |
| - The Space provides the complete working demo | |
| - The notebook contains the full training pipeline and reproducible steps | |
| - This repository is for model checkpoints only | |
| - HF Jobs are not publicly accessible by design - the notebook serves as the verifiable training record |