Instructions to use Salesforce/codegen-350M-mono with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/codegen-350M-mono with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Salesforce/codegen-350M-mono")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Salesforce/codegen-350M-mono with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Salesforce/codegen-350M-mono" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Salesforce/codegen-350M-mono", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Salesforce/codegen-350M-mono
- SGLang
How to use Salesforce/codegen-350M-mono 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 "Salesforce/codegen-350M-mono" \ --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": "Salesforce/codegen-350M-mono", "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 "Salesforce/codegen-350M-mono" \ --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": "Salesforce/codegen-350M-mono", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Salesforce/codegen-350M-mono with Docker Model Runner:
docker model run hf.co/Salesforce/codegen-350M-mono
Fine-tuning for writing code from prompts
#2
by Johnguitarist - opened
Instead of using this model for code completion in a causal LM context, how might I use it to write code from prompts?
Example input: 'function to scale numerical features in a dataframe'
would return a python function to perform this.
I've been trying to fine-tune this for a while now but it seems CausalLM isn't the mode I was expecting,
What imports and steps would I need to achieve results like I described above?
Many thanks!
bomp