Instructions to use Ammad1Ali/alex-gptq-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ammad1Ali/alex-gptq-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ammad1Ali/alex-gptq-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ammad1Ali/alex-gptq-4bit") model = AutoModelForCausalLM.from_pretrained("Ammad1Ali/alex-gptq-4bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ammad1Ali/alex-gptq-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ammad1Ali/alex-gptq-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ammad1Ali/alex-gptq-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ammad1Ali/alex-gptq-4bit
- SGLang
How to use Ammad1Ali/alex-gptq-4bit 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 "Ammad1Ali/alex-gptq-4bit" \ --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": "Ammad1Ali/alex-gptq-4bit", "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 "Ammad1Ali/alex-gptq-4bit" \ --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": "Ammad1Ali/alex-gptq-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Ammad1Ali/alex-gptq-4bit with Docker Model Runner:
docker model run hf.co/Ammad1Ali/alex-gptq-4bit
Download model.safetensors from Ammad1Ali/alex-gptq-4bit: direct link, hf CLI and curl.
- Browser
- Download file 3.9 GB
-
https://huggingface.co/Ammad1Ali/alex-gptq-4bit/resolve/12f56e1239d9d5840ad2fa65dc5fc08133d0b2ca/model.safetensors
- Command line
-
hf download hf://Ammad1Ali/alex-gptq-4bit@12f56e1239d9d5840ad2fa65dc5fc08133d0b2ca/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Ammad1Ali/alex-gptq-4bit/resolve/12f56e1239d9d5840ad2fa65dc5fc08133d0b2ca/model.safetensors
3.9 GB
- Xet hash:
- 238effbde2cfe73fbd327afc9643baa21aa041274dd65d7efec5cddc69570494
- Size of remote file:
- 3.9 GB
- SHA256:
- 854d0f3030951253a68ce79b29266a234f32127104d2400e15b569f550a25ee0
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