Instructions to use smpanaro/gpt2-large-AutoGPTQ-4bit-128g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

smpanaro commited on Feb 28, 2024

Commit

aa5636f

verified ·

1 Parent(s): c0f2727

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -28,4 +28,4 @@ model = AutoGPTQForCausalLM.from_quantized(model_name)
 |[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)</sub>

 |[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>