Instructions to use Gege24/zay-starcoder3b-dpo-pathD-2gpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Gege24/zay-starcoder3b-dpo-pathD-2gpu with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder2-3b") model = PeftModel.from_pretrained(base_model, "Gege24/zay-starcoder3b-dpo-pathD-2gpu") - Transformers
How to use Gege24/zay-starcoder3b-dpo-pathD-2gpu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Gege24/zay-starcoder3b-dpo-pathD-2gpu")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Gege24/zay-starcoder3b-dpo-pathD-2gpu", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Gege24/zay-starcoder3b-dpo-pathD-2gpu with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Gege24/zay-starcoder3b-dpo-pathD-2gpu" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Gege24/zay-starcoder3b-dpo-pathD-2gpu", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Gege24/zay-starcoder3b-dpo-pathD-2gpu
- SGLang
How to use Gege24/zay-starcoder3b-dpo-pathD-2gpu 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 "Gege24/zay-starcoder3b-dpo-pathD-2gpu" \ --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": "Gege24/zay-starcoder3b-dpo-pathD-2gpu", "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 "Gege24/zay-starcoder3b-dpo-pathD-2gpu" \ --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": "Gege24/zay-starcoder3b-dpo-pathD-2gpu", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Gege24/zay-starcoder3b-dpo-pathD-2gpu with Docker Model Runner:
docker model run hf.co/Gege24/zay-starcoder3b-dpo-pathD-2gpu
- Xet hash:
- 04fe30ea3b129c579cdfda026cb547be221e2a6100462dfa26fe58bb893839df
- Size of remote file:
- 7.12 kB
- SHA256:
- 9042f3417657c088979652fcee58d58ea78eae57afd750f0e3092ecdc42ca03e
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