Instructions to use Zzyy2000/qwen3-4b-grpo-main with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zzyy2000/qwen3-4b-grpo-main with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Zzyy2000/qwen3-4b-grpo-main")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Zzyy2000/qwen3-4b-grpo-main", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Zzyy2000/qwen3-4b-grpo-main with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Zzyy2000/qwen3-4b-grpo-main" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zzyy2000/qwen3-4b-grpo-main", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Zzyy2000/qwen3-4b-grpo-main
- SGLang
How to use Zzyy2000/qwen3-4b-grpo-main 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 "Zzyy2000/qwen3-4b-grpo-main" \ --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": "Zzyy2000/qwen3-4b-grpo-main", "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 "Zzyy2000/qwen3-4b-grpo-main" \ --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": "Zzyy2000/qwen3-4b-grpo-main", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Zzyy2000/qwen3-4b-grpo-main with Docker Model Runner:
docker model run hf.co/Zzyy2000/qwen3-4b-grpo-main
Qwen3-4B GRPO checkpoint bundle
This public repository contains the selected final/root GRPO checkpoints from the Qwen3 safety experiment. They were initialized from Qwen3-4B-Base, not from the non-Base Qwen3-4B instruct/chat model.
The bundle contains four independently loadable subdirectories:
grpo_qwen3_4b_basegrpo_qwen3_4b_base_generalgrpo_qwen3_4b_midtraingrpo_qwen3_4b_midtrain_general
Only the selected root/final model weights are included; intermediate checkpoint-* directories are intentionally omitted. Each subdirectory includes its model configuration and tokenizer files.
Example:
from transformers import AutoTokenizer, AutoModelForCausalLM
path = "Zzyy2000/qwen3-4b-grpo-main/grpo_qwen3_4b_midtrain"
tok = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype="auto", device_map="auto")
See the handoff code repository for the training and evaluation scripts: https://github.com/ZhengyueZhao/qwen3-midtrain-handoff
Model tree for Zzyy2000/qwen3-4b-grpo-main
Base model
Qwen/Qwen3-4B-Base