Text Generation
Transformers
Safetensors
English
Chinese
sdar
math
reasoning
diffusion
conversational
custom_code
Instructions to use OpenMOSS-Team/DiRL-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/DiRL-8B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenMOSS-Team/DiRL-8B-Instruct", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("OpenMOSS-Team/DiRL-8B-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenMOSS-Team/DiRL-8B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenMOSS-Team/DiRL-8B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenMOSS-Team/DiRL-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpenMOSS-Team/DiRL-8B-Instruct
- SGLang
How to use OpenMOSS-Team/DiRL-8B-Instruct 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 "OpenMOSS-Team/DiRL-8B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenMOSS-Team/DiRL-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "OpenMOSS-Team/DiRL-8B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenMOSS-Team/DiRL-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OpenMOSS-Team/DiRL-8B-Instruct with Docker Model Runner:
docker model run hf.co/OpenMOSS-Team/DiRL-8B-Instruct
Commit History
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Add pipeline_tag and library_name to metadata
Verified signatureHi! I'm Niels from the Hugging Face community science team. This pull request improves the model card by adding relevant metadata to the YAML header. Specifically: - `pipeline_tag: text-generation`: Categorizes the model for the text generation task. - `library_name: transformers`: Enables the "Use in Transformers" button on the Hub, as the model uses `auto_map` in its `config.json`. These changes help users discover the model more easily and provide guidance on how to use it within the Hugging Face ecosystem.14b37d7
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