Instructions to use lebe1/opt-125m-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lebe1/opt-125m-2bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lebe1/opt-125m-2bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lebe1/opt-125m-2bit") model = AutoModelForCausalLM.from_pretrained("lebe1/opt-125m-2bit") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lebe1/opt-125m-2bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lebe1/opt-125m-2bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lebe1/opt-125m-2bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lebe1/opt-125m-2bit
- SGLang
How to use lebe1/opt-125m-2bit 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 "lebe1/opt-125m-2bit" \ --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": "lebe1/opt-125m-2bit", "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 "lebe1/opt-125m-2bit" \ --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": "lebe1/opt-125m-2bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lebe1/opt-125m-2bit with Docker Model Runner:
docker model run hf.co/lebe1/opt-125m-2bit
run
!pip install optimum
!pip install auto-gptq
from transformers import pipeline, AutoTokenizer
messages = [
{"role": "user", "content": "Who are you?"},
]
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
pipe = pipeline("text-generation", model="lebe1/opt-125m-2bit", tokenizer=tokenizer, trust_remote_code=True)
Define a chat template
chat_template = """{% for message in messages %}{{ message.role }}: {{ message.content }}{% endfor %}"""
Set the tokenizer's chat template
tokenizer.chat_template = chat_template
Apply the chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
Generate text using the formatted prompt
generated_text = pipe(prompt, max_new_tokens=50)[0]['generated_text']
print(generated_text)