Text Generation
Transformers
PyTorch
English
gpt2
GPT-2
fine-tuned
language-model
text-generation-inference
Instructions to use rahul77/gpt-2-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rahul77/gpt-2-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rahul77/gpt-2-finetune")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rahul77/gpt-2-finetune") model = AutoModelForCausalLM.from_pretrained("rahul77/gpt-2-finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rahul77/gpt-2-finetune with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rahul77/gpt-2-finetune" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rahul77/gpt-2-finetune", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rahul77/gpt-2-finetune
- SGLang
How to use rahul77/gpt-2-finetune 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 "rahul77/gpt-2-finetune" \ --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": "rahul77/gpt-2-finetune", "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 "rahul77/gpt-2-finetune" \ --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": "rahul77/gpt-2-finetune", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rahul77/gpt-2-finetune with Docker Model Runner:
docker model run hf.co/rahul77/gpt-2-finetune
Download pytorch_model.bin from rahul77/gpt-2-finetune: direct link, hf CLI and curl.
- Browser
- Download file 249 MB
-
https://huggingface.co/rahul77/gpt-2-finetune/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rahul77/gpt-2-finetune/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rahul77/gpt-2-finetune/resolve/main/pytorch_model.bin
249 MB
- Xet hash:
- adab256f8036ebed60dcb3b54f3bbfeabbb6e2956d3f4818c89684dc19808438
- Size of remote file:
- 249 MB
- SHA256:
- 33e596c6e7c4d8cbb37d7116caab786ae303eaef6450523a28676547f5a247af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.