Instructions to use amandyk/QazGPT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amandyk/QazGPT2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="amandyk/QazGPT2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("amandyk/QazGPT2") model = AutoModelForCausalLM.from_pretrained("amandyk/QazGPT2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use amandyk/QazGPT2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "amandyk/QazGPT2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amandyk/QazGPT2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/amandyk/QazGPT2
- SGLang
How to use amandyk/QazGPT2 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 "amandyk/QazGPT2" \ --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": "amandyk/QazGPT2", "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 "amandyk/QazGPT2" \ --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": "amandyk/QazGPT2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use amandyk/QazGPT2 with Docker Model Runner:
docker model run hf.co/amandyk/QazGPT2
Download optimizer.pt from amandyk/QazGPT2: direct link, hf CLI and curl.
- Browser
- Download file 1.3 GB
-
https://huggingface.co/amandyk/QazGPT2/resolve/refs%2Fpr%2F2/optimizer.pt
- Command line
-
hf download hf://amandyk/QazGPT2@refs/pr/2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/amandyk/QazGPT2/resolve/refs%2Fpr%2F2/optimizer.pt
1.3 GB
- Xet hash:
- c8b3ef69d5b3954f25bd3c0e8b9b14842ea7923c5d8ce6655f7aecfacc3d8617
- Size of remote file:
- 1.3 GB
- SHA256:
- 55bfd8bc902d1f1606436bd7e3f1fee0f24caa08b2a97feb5c34f1a7b93a037a
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