Instructions to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ControlLLM/Llama3.1-8B-OpenMath16-Instruct")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ControlLLM/Llama3.1-8B-OpenMath16-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ControlLLM/Llama3.1-8B-OpenMath16-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ControlLLM/Llama3.1-8B-OpenMath16-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ControlLLM/Llama3.1-8B-OpenMath16-Instruct

SGLang

How to use ControlLLM/Llama3.1-8B-OpenMath16-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 "ControlLLM/Llama3.1-8B-OpenMath16-Instruct" \
    --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": "ControlLLM/Llama3.1-8B-OpenMath16-Instruct",
		"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 "ControlLLM/Llama3.1-8B-OpenMath16-Instruct" \
        --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": "ControlLLM/Llama3.1-8B-OpenMath16-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with Docker Model Runner:
```
docker model run hf.co/ControlLLM/Llama3.1-8B-OpenMath16-Instruct
```

Add library name, pipeline tag, and link to paper

by nielsr HF Staff - opened Jan 25, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-1

nielsr

Jan 25, 2025

This PR adds the pipeline_tag to the model card, ensuring the model can be found at https://huggingface.co/models?pipeline_tag=text-generation&sort=trending.
It also adds the library_name: transformers to ensure the "how to use button" shows up. Finally, it adds the link to the paper page of the model
to improve its discoverability.

Add library name, pipeline tag, and link to papere530760a

hawei changed pull request status to merged Jan 25, 2025

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