Instructions to use facebook/MobileLLM-125M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/MobileLLM-125M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="facebook/MobileLLM-125M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("facebook/MobileLLM-125M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use facebook/MobileLLM-125M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "facebook/MobileLLM-125M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "facebook/MobileLLM-125M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/facebook/MobileLLM-125M
- SGLang
How to use facebook/MobileLLM-125M 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 "facebook/MobileLLM-125M" \ --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": "facebook/MobileLLM-125M", "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 "facebook/MobileLLM-125M" \ --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": "facebook/MobileLLM-125M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use facebook/MobileLLM-125M with Docker Model Runner:
docker model run hf.co/facebook/MobileLLM-125M
Reference Scripts for Fine-Tuning Pretrained LLMs on Downstream Tasks
#6
by venkateshdas - opened
I'm looking for guidance and reference scripts to fine-tune pretrained large language models (LLMs) on specific downstream tasks. My goal is to adapt these lighter LLMs for use cases such as text classification, question answering, and other specialized tasks. If anyone has sample scripts, best practices, or tips for setting up datasets and configuring hyperparameters, please share your insights! Any advice on performance optimization or evaluation methods would also be greatly appreciated. Thank you!