Instructions to use Xwin-LM/XwinCoder-34B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Xwin-LM/XwinCoder-34B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Xwin-LM/XwinCoder-34B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Xwin-LM/XwinCoder-34B") model = AutoModelForCausalLM.from_pretrained("Xwin-LM/XwinCoder-34B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Xwin-LM/XwinCoder-34B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Xwin-LM/XwinCoder-34B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Xwin-LM/XwinCoder-34B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Xwin-LM/XwinCoder-34B
- SGLang
How to use Xwin-LM/XwinCoder-34B 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 "Xwin-LM/XwinCoder-34B" \ --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": "Xwin-LM/XwinCoder-34B", "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 "Xwin-LM/XwinCoder-34B" \ --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": "Xwin-LM/XwinCoder-34B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Xwin-LM/XwinCoder-34B with Docker Model Runner:
docker model run hf.co/Xwin-LM/XwinCoder-34B
Download pytorch_model-00002-of-00007.bin from Xwin-LM/XwinCoder-34B: direct link, hf CLI and curl.
- Browser
- Download file 9.69 GB
-
https://huggingface.co/Xwin-LM/XwinCoder-34B/resolve/main/pytorch_model-00002-of-00007.bin
- Command line
-
hf download hf://Xwin-LM/XwinCoder-34B/pytorch_model-00002-of-00007.bin
-
curl -L -o pytorch_model-00002-of-00007.bin https://huggingface.co/Xwin-LM/XwinCoder-34B/resolve/main/pytorch_model-00002-of-00007.bin
9.69 GB
- Xet hash:
- 9e4ac8086b766fe6ecd7610febdcb45e798b34dea9f934fb42b6082b729f0abe
- Size of remote file:
- 9.69 GB
- SHA256:
- 94ef227b989a32d054a8c409fac283c978c28a621b73c68a91ddb68a68dfe0e8
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