Instructions to use Salesforce/codegen25-7b-multi_P with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/codegen25-7b-multi_P with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Salesforce/codegen25-7b-multi_P")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen25-7b-multi_P") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen25-7b-multi_P") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Salesforce/codegen25-7b-multi_P with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Salesforce/codegen25-7b-multi_P" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Salesforce/codegen25-7b-multi_P", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Salesforce/codegen25-7b-multi_P
- SGLang
How to use Salesforce/codegen25-7b-multi_P 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/codegen25-7b-multi_P" \ --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/codegen25-7b-multi_P", "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/codegen25-7b-multi_P" \ --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/codegen25-7b-multi_P", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Salesforce/codegen25-7b-multi_P with Docker Model Runner:
docker model run hf.co/Salesforce/codegen25-7b-multi_P
Make _decode compatible with PreTrainedTokenizerBase
#8
by Vinno97 - opened
CodeGen25Tokenizer correctly implements the _decode interface from PreTrainedTokenizer with the following signature
def _decode(self, token_ids: List[int], ...,) -> str:
...
PreTrainedTokenizer, however, incorrectly shadows the _decode function of its base class PreTrainedTokenizerBase, which is defined like this:
def _decode(self, token_ids: Union[int, List[int]], ...,) -> str:
...
As a result, CodeGen25Tokenizer cannot be used as a drop-in tokenizer in some codebases (like TGI). This fix doesn't break any previous behaviour, but simply allows decode to also accept plain int values instead of only list[int].
rooa changed pull request status to merged