Text Generation
Transformers
Safetensors
gpt_bigcode
Generated from Trainer
text-generation-inference
Instructions to use nj180280/tinystarcoder-rlhf-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nj180280/tinystarcoder-rlhf-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nj180280/tinystarcoder-rlhf-model")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nj180280/tinystarcoder-rlhf-model") model = AutoModelForCausalLM.from_pretrained("nj180280/tinystarcoder-rlhf-model") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nj180280/tinystarcoder-rlhf-model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nj180280/tinystarcoder-rlhf-model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nj180280/tinystarcoder-rlhf-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nj180280/tinystarcoder-rlhf-model
- SGLang
How to use nj180280/tinystarcoder-rlhf-model 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 "nj180280/tinystarcoder-rlhf-model" \ --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": "nj180280/tinystarcoder-rlhf-model", "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 "nj180280/tinystarcoder-rlhf-model" \ --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": "nj180280/tinystarcoder-rlhf-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nj180280/tinystarcoder-rlhf-model with Docker Model Runner:
docker model run hf.co/nj180280/tinystarcoder-rlhf-model
- Xet hash:
- dafe1f5d9221e4c5e7d9bf33703c1cf3958bfe7860f0a0387a2164d2760d813b
- Size of remote file:
- 657 MB
- SHA256:
- b3ac0f399c8b250b512022cf83dff951334e358a8d05f0f53e8c14bb76a15617
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