Instructions to use BashitAli/GPT_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BashitAli/GPT_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BashitAli/GPT_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BashitAli/GPT_model") model = AutoModelForCausalLM.from_pretrained("BashitAli/GPT_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BashitAli/GPT_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BashitAli/GPT_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BashitAli/GPT_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BashitAli/GPT_model
- SGLang
How to use BashitAli/GPT_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 "BashitAli/GPT_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": "BashitAli/GPT_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 "BashitAli/GPT_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": "BashitAli/GPT_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BashitAli/GPT_model with Docker Model Runner:
docker model run hf.co/BashitAli/GPT_model
Download tokenizer_config.json from BashitAli/GPT_model: direct link, hf CLI and curl.
- Browser
- Download file 450 Bytes
-
https://huggingface.co/BashitAli/GPT_model/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://BashitAli/GPT_model/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/BashitAli/GPT_model/resolve/main/tokenizer_config.json
450 Bytes
| { | |
| "add_prefix_space": false, | |
| "bos_token": "<|endoftext|>", | |
| "eos_token": "<|endoftext|>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "name_or_path": "EleutherAI/pythia-2.8b", | |
| "special_tokens_map_file": "/admin/home-hailey/.cache/huggingface/hub/models--EleutherAI--gpt-neox-20b/snapshots/4e49eadb5d14bd22f314ec3f45b69a87b88c7691/special_tokens_map.json", | |
| "tokenizer_class": "GPTNeoXTokenizer", | |
| "unk_token": "<|endoftext|>" | |
| } | |