Instructions to use Asimok/KGLQA-KeySentenceSelect-NCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Asimok/KGLQA-KeySentenceSelect-NCR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Asimok/KGLQA-KeySentenceSelect-NCR")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Asimok/KGLQA-KeySentenceSelect-NCR") model = AutoModelForCausalLM.from_pretrained("Asimok/KGLQA-KeySentenceSelect-NCR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Asimok/KGLQA-KeySentenceSelect-NCR with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Asimok/KGLQA-KeySentenceSelect-NCR" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Asimok/KGLQA-KeySentenceSelect-NCR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Asimok/KGLQA-KeySentenceSelect-NCR
- SGLang
How to use Asimok/KGLQA-KeySentenceSelect-NCR 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 "Asimok/KGLQA-KeySentenceSelect-NCR" \ --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": "Asimok/KGLQA-KeySentenceSelect-NCR", "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 "Asimok/KGLQA-KeySentenceSelect-NCR" \ --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": "Asimok/KGLQA-KeySentenceSelect-NCR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Asimok/KGLQA-KeySentenceSelect-NCR with Docker Model Runner:
docker model run hf.co/Asimok/KGLQA-KeySentenceSelect-NCR
Download tokenizer.model from Asimok/KGLQA-KeySentenceSelect-NCR: direct link, hf CLI and curl.
- Browser
- Download file 1.27 MB
-
https://huggingface.co/Asimok/KGLQA-KeySentenceSelect-NCR/resolve/main/tokenizer.model
- Command line
-
hf download hf://Asimok/KGLQA-KeySentenceSelect-NCR/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/Asimok/KGLQA-KeySentenceSelect-NCR/resolve/main/tokenizer.model
1.27 MB
- Xet hash:
- 62e262e9ffe8e87ed27ca32e548e7d978121c163661f679431543b0040f9bc55
- Size of remote file:
- 1.27 MB
- SHA256:
- 0b716a618c9e7c45648f91d997431eba3b0ff111b17ce7b777280ed771a49f95
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.