Instructions to use berkeley-nest/Starling-LM-7B-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use berkeley-nest/Starling-LM-7B-alpha with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="berkeley-nest/Starling-LM-7B-alpha") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("berkeley-nest/Starling-LM-7B-alpha") model = AutoModelForCausalLM.from_pretrained("berkeley-nest/Starling-LM-7B-alpha") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use berkeley-nest/Starling-LM-7B-alpha with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "berkeley-nest/Starling-LM-7B-alpha" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "berkeley-nest/Starling-LM-7B-alpha", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/berkeley-nest/Starling-LM-7B-alpha
- SGLang
How to use berkeley-nest/Starling-LM-7B-alpha 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 "berkeley-nest/Starling-LM-7B-alpha" \ --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": "berkeley-nest/Starling-LM-7B-alpha", "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 "berkeley-nest/Starling-LM-7B-alpha" \ --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": "berkeley-nest/Starling-LM-7B-alpha", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use berkeley-nest/Starling-LM-7B-alpha with Docker Model Runner:
docker model run hf.co/berkeley-nest/Starling-LM-7B-alpha
Issues in the tokenizer
how <sep> token id 32002 causes seg faults due to out of bounds accesses?
Can confirm as well during DPO training, it seems like the tokenizer's addition of <sep> is not in the embedding matrix of size 32002. The maximum id should be 32001.
Yeah they also show this in dpo training.
I just add unk and pad token and they show 32004 tokenizer len.
Also the dpo run for a few steps and then show this error.
it showing this error
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.
Here is the Dpo code
https://colab.research.google.com/drive/1uC7LohnGJF-Y4vzPz14z6OgZknkeZqD2?usp=sharing
hello, Imran ullah, I encounter the same problem, may I know how you overcame this problem? by modify the tokenizer config.json or modify the code ?