Text Generation
Transformers
PyTorch
English
llama
code
music
finance
text-generation-inference
8-bit precision
Instructions to use alexpaul/QI-Large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexpaul/QI-Large-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="alexpaul/QI-Large-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("alexpaul/QI-Large-v1") model = AutoModelForCausalLM.from_pretrained("alexpaul/QI-Large-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use alexpaul/QI-Large-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "alexpaul/QI-Large-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alexpaul/QI-Large-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/alexpaul/QI-Large-v1
- SGLang
How to use alexpaul/QI-Large-v1 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 "alexpaul/QI-Large-v1" \ --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": "alexpaul/QI-Large-v1", "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 "alexpaul/QI-Large-v1" \ --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": "alexpaul/QI-Large-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use alexpaul/QI-Large-v1 with Docker Model Runner:
docker model run hf.co/alexpaul/QI-Large-v1
Download pytorch_model.bin from alexpaul/QI-Large-v1: direct link, hf CLI and curl.
- Browser
- Download file 7.01 GB
-
https://huggingface.co/alexpaul/QI-Large-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://alexpaul/QI-Large-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/alexpaul/QI-Large-v1/resolve/main/pytorch_model.bin
7.01 GB
- Xet hash:
- 5d76ef172464209032db88c7e4458b38736233b95ca530a0eb11ba7ea7187f5a
- Size of remote file:
- 7.01 GB
- SHA256:
- 365c581a46ebb06bd2cc4f4ac4a21bb150623c56e72a8894e3a72756cd79d9d4
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