Instructions to use diffutron/DiffutronLM-0.3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diffutron/DiffutronLM-0.3B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="diffutron/DiffutronLM-0.3B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("diffutron/DiffutronLM-0.3B-Instruct") model = AutoModelForMaskedLM.from_pretrained("diffutron/DiffutronLM-0.3B-Instruct") 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
- vLLM
How to use diffutron/DiffutronLM-0.3B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "diffutron/DiffutronLM-0.3B-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": "diffutron/DiffutronLM-0.3B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/diffutron/DiffutronLM-0.3B-Instruct
- SGLang
How to use diffutron/DiffutronLM-0.3B-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 "diffutron/DiffutronLM-0.3B-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": "diffutron/DiffutronLM-0.3B-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 "diffutron/DiffutronLM-0.3B-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": "diffutron/DiffutronLM-0.3B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use diffutron/DiffutronLM-0.3B-Instruct with Docker Model Runner:
docker model run hf.co/diffutron/DiffutronLM-0.3B-Instruct
| {% if messages[0]['role'] == 'system' %} | |
| [SYS] | |
| {{ messages[0]['content'] | trim }} | |
| [/SYS] | |
| {% set loop_messages = messages[1:] %} | |
| {% else %} | |
| {% set loop_messages = messages %} | |
| {% endif -%} | |
| {%- for message in loop_messages %} | |
| {% if message['role'] == 'user' %} | |
| [Question] | |
| {{ message['content'] | trim }} | |
| [/Question] | |
| {% elif message['role'] == 'assistant' %} | |
| [Answer] | |
| {{ message['content'] | trim }} | |
| [/Answer] | |
| {% endif %} | |
| {% endfor -%} | |
| {%- if add_generation_prompt and (loop_messages | length == 0 or loop_messages[-1]['role'] != 'assistant') %} | |
| [Answer] | |
| {% endif %} | |