Instructions to use stabilityai/stablelm-zephyr-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stabilityai/stablelm-zephyr-3b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="stabilityai/stablelm-zephyr-3b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-zephyr-3b") model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-zephyr-3b", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use stabilityai/stablelm-zephyr-3b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "stabilityai/stablelm-zephyr-3b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "stabilityai/stablelm-zephyr-3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/stabilityai/stablelm-zephyr-3b
- SGLang
How to use stabilityai/stablelm-zephyr-3b 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 "stabilityai/stablelm-zephyr-3b" \ --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": "stabilityai/stablelm-zephyr-3b", "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 "stabilityai/stablelm-zephyr-3b" \ --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": "stabilityai/stablelm-zephyr-3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use stabilityai/stablelm-zephyr-3b with Docker Model Runner:
docker model run hf.co/stabilityai/stablelm-zephyr-3b
recommended prompt structure
Hello,
I've been experimenting with "stablelm-zephyr-3b" for quite a while now - with mediocre results until I (accidentally) formatted my prompt without the recommended <|endoftext|> - and suddenly, I got exciting results!
Hence my question: is the prompt example shown in the model card really correct?
Hi, will you please share your input prompt format. I am also trying out various prompt styles to get a better result for a common input prompt. It would be helpful to know yours :)
Sure, here is what I am using right now (based on the example from the model card):
<|user|>
List 3 synonyms for the word "tiny"
<|assistant|>
1. Dwarf
2. Little
3. Petite
Thus, simply omit the <|endoftext|>, that's it!
This is a good question. I would like to know as well.
Me too :)