Instructions to use OpenMOSS-Team/DiRL-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OpenMOSS-Team/DiRL-8B-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Add pipeline_tag and library_name to metadata

by nielsr HF Staff - opened Dec 31, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-1

Add pipeline_tag and library_name to metadata14b37d7d

nielsr

Dec 31, 2025

Hi! 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.

Auraithm

Dec 31, 2025

OK, thank you.

Auraithm changed pull request status to merged Dec 31, 2025

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