Text Generation
Transformers
PyTorch
English
code
codegen
Diff Model
causal-lm
code-generation
The Pile
Instructions to use CarperAI/diff-codegen-350m-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CarperAI/diff-codegen-350m-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CarperAI/diff-codegen-350m-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CarperAI/diff-codegen-350m-v1") model = AutoModelForCausalLM.from_pretrained("CarperAI/diff-codegen-350m-v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use CarperAI/diff-codegen-350m-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CarperAI/diff-codegen-350m-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CarperAI/diff-codegen-350m-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CarperAI/diff-codegen-350m-v1
- SGLang
How to use CarperAI/diff-codegen-350m-v1 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 "CarperAI/diff-codegen-350m-v1" \ --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": "CarperAI/diff-codegen-350m-v1", "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 "CarperAI/diff-codegen-350m-v1" \ --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": "CarperAI/diff-codegen-350m-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CarperAI/diff-codegen-350m-v1 with Docker Model Runner:
docker model run hf.co/CarperAI/diff-codegen-350m-v1
- Xet hash:
- 56f438cf9fc27743e47e9b2c042895394b84bd4741a0a446ad4d53336ccef5fd
- Size of remote file:
- 794 MB
- SHA256:
- 13764f9165107f0e69b2d1b4ada7a14ccc35f1f5b355d162a96b594a8967fb72
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.