Instructions to use smpanaro/gpt2-large-AutoGPTQ-4bit-128g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smpanaro/gpt2-large-AutoGPTQ-4bit-128g with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="smpanaro/gpt2-large-AutoGPTQ-4bit-128g")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("smpanaro/gpt2-large-AutoGPTQ-4bit-128g") model = AutoModelForCausalLM.from_pretrained("smpanaro/gpt2-large-AutoGPTQ-4bit-128g", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use smpanaro/gpt2-large-AutoGPTQ-4bit-128g with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "smpanaro/gpt2-large-AutoGPTQ-4bit-128g" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "smpanaro/gpt2-large-AutoGPTQ-4bit-128g", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/smpanaro/gpt2-large-AutoGPTQ-4bit-128g
- SGLang
How to use smpanaro/gpt2-large-AutoGPTQ-4bit-128g 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 "smpanaro/gpt2-large-AutoGPTQ-4bit-128g" \ --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": "smpanaro/gpt2-large-AutoGPTQ-4bit-128g", "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 "smpanaro/gpt2-large-AutoGPTQ-4bit-128g" \ --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": "smpanaro/gpt2-large-AutoGPTQ-4bit-128g", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use smpanaro/gpt2-large-AutoGPTQ-4bit-128g with Docker Model Runner:
docker model run hf.co/smpanaro/gpt2-large-AutoGPTQ-4bit-128g
| license: mit | |
| datasets: | |
| - wikitext | |
| [gpt2-large](https://huggingface.co/openai-community/gpt2-large) quantized to 4-bit using [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ). | |
| To use, first install AutoGPTQ: | |
| ```shell | |
| pip install auto-gptq | |
| ``` | |
| Then load the model from the hub: | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig | |
| model_name = "smpanaro/gpt2-large-AutoGPTQ-4bit-128g" | |
| model = AutoGPTQForCausalLM.from_quantized(model_name) | |
| ``` | |
| |Model|4-Bit Perplexity|16-Bit Perplexity|Delta| | |
| |--|--|--|--| | |
| |[smpanaro/gpt2-AutoGPTQ-4bit-128g](https://huggingface.co/smpanaro/gpt2-AutoGPTQ-4bit-128g)|26.5000|25.1875|1.3125| | |
| |[smpanaro/gpt2-medium-AutoGPTQ-4bit-128g](https://huggingface.co/smpanaro/gpt2-medium-AutoGPTQ-4bit-128g)|19.1719|18.4739|0.698| | |
| |smpanaro/gpt2-large-AutoGPTQ-4bit-128g|16.6875|16.4541|0.2334| | |
| |[smpanaro/gpt2-xl-AutoGPTQ-4bit-128g](https://huggingface.co/smpanaro/gpt2-xl-AutoGPTQ-4bit-128g)|14.9297|14.7951|0.1346| | |
| <sub>Wikitext perplexity measured as in the [huggingface docs](https://huggingface.co/docs/transformers/en/perplexity), lower is better</sub> |