Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
Instructions to use kbalde/codeparrot-ds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kbalde/codeparrot-ds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kbalde/codeparrot-ds")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kbalde/codeparrot-ds") model = AutoModelForCausalLM.from_pretrained("kbalde/codeparrot-ds", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kbalde/codeparrot-ds with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kbalde/codeparrot-ds" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kbalde/codeparrot-ds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kbalde/codeparrot-ds
- SGLang
How to use kbalde/codeparrot-ds 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 "kbalde/codeparrot-ds" \ --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": "kbalde/codeparrot-ds", "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 "kbalde/codeparrot-ds" \ --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": "kbalde/codeparrot-ds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kbalde/codeparrot-ds with Docker Model Runner:
docker model run hf.co/kbalde/codeparrot-ds
Download training_args.bin from kbalde/codeparrot-ds: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/kbalde/codeparrot-ds/resolve/main/training_args.bin
- Command line
-
hf download hf://kbalde/codeparrot-ds/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kbalde/codeparrot-ds/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 579bb549dcb55dd9f6666b88d6f2ad19c327e9887517bea3fdc22027f1923cb6
- Size of remote file:
- 3.38 kB
- SHA256:
- 9380991ec44c9f892438473253a995192c10df989fba9d48aa114717d0b518a4
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