Instructions to use MegaScience/Qwen2.5-7B-MegaScience with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MegaScience/Qwen2.5-7B-MegaScience with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MegaScience/Qwen2.5-7B-MegaScience") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("MegaScience/Qwen2.5-7B-MegaScience") model = AutoModelForMultimodalLM.from_pretrained("MegaScience/Qwen2.5-7B-MegaScience") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MegaScience/Qwen2.5-7B-MegaScience with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MegaScience/Qwen2.5-7B-MegaScience" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MegaScience/Qwen2.5-7B-MegaScience", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MegaScience/Qwen2.5-7B-MegaScience
- SGLang
How to use MegaScience/Qwen2.5-7B-MegaScience 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 "MegaScience/Qwen2.5-7B-MegaScience" \ --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": "MegaScience/Qwen2.5-7B-MegaScience", "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 "MegaScience/Qwen2.5-7B-MegaScience" \ --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": "MegaScience/Qwen2.5-7B-MegaScience", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use MegaScience/Qwen2.5-7B-MegaScience with Docker Model Runner:
docker model run hf.co/MegaScience/Qwen2.5-7B-MegaScience
Improve model card: Add library_name, GitHub link, abstract, and usage example
#1
by nielsr HF Staff - opened
This PR improves the model card for MegaScience/Qwen2.5-7B-MegaScience by:
- Adding the
library_name: transformersmetadata tag, which enables the "How to use" widget on the model page. - Including a concise abstract of the paper to provide immediate context for users.
- Adding a direct link to the project's GitHub repository for easier access to the code and further documentation.
- Including a basic Python code snippet for text generation to demonstrate immediate usage with the
transformerslibrary.
Thank you very much for your effort in refining the README.
Vfrz changed pull request status to merged