Instructions to use Idred/BlastRadius-GRPO-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Update README.md

by ainey1116 - opened Apr 27

base: refs/heads/main

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from: refs/pr/1

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@@ -1,21 +1,53 @@
 # 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

+---
+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