Text Generation
Transformers
PyTorch
Safetensors
code
gpt_neox
causal-lm
Eval Results (legacy)
text-generation-inference
Instructions to use vodkaslime/test-repo-stablecode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vodkaslime/test-repo-stablecode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="vodkaslime/test-repo-stablecode")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("vodkaslime/test-repo-stablecode") model = AutoModelForCausalLM.from_pretrained("vodkaslime/test-repo-stablecode", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use vodkaslime/test-repo-stablecode with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vodkaslime/test-repo-stablecode" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vodkaslime/test-repo-stablecode", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vodkaslime/test-repo-stablecode
- SGLang
How to use vodkaslime/test-repo-stablecode 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 "vodkaslime/test-repo-stablecode" \ --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": "vodkaslime/test-repo-stablecode", "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 "vodkaslime/test-repo-stablecode" \ --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": "vodkaslime/test-repo-stablecode", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use vodkaslime/test-repo-stablecode with Docker Model Runner:
docker model run hf.co/vodkaslime/test-repo-stablecode
- Xet hash:
- 02cb2143c6ab7ac5f055a0fa41520ef83b8c20ff681bcbcf2b31a3f8e4364576
- Size of remote file:
- 4.16 kB
- SHA256:
- 142478985dadb6f14a40dcb1885e8cd0d0a543b018a64abaa6a968bfe45980f1
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