Instructions to use nlpai-lab/kullm-polyglot-5.8b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpai-lab/kullm-polyglot-5.8b-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nlpai-lab/kullm-polyglot-5.8b-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nlpai-lab/kullm-polyglot-5.8b-v2") model = AutoModelForCausalLM.from_pretrained("nlpai-lab/kullm-polyglot-5.8b-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nlpai-lab/kullm-polyglot-5.8b-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nlpai-lab/kullm-polyglot-5.8b-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nlpai-lab/kullm-polyglot-5.8b-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nlpai-lab/kullm-polyglot-5.8b-v2
- SGLang
How to use nlpai-lab/kullm-polyglot-5.8b-v2 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 "nlpai-lab/kullm-polyglot-5.8b-v2" \ --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": "nlpai-lab/kullm-polyglot-5.8b-v2", "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 "nlpai-lab/kullm-polyglot-5.8b-v2" \ --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": "nlpai-lab/kullm-polyglot-5.8b-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nlpai-lab/kullm-polyglot-5.8b-v2 with Docker Model Runner:
docker model run hf.co/nlpai-lab/kullm-polyglot-5.8b-v2
Download tokenizer_config.json from nlpai-lab/kullm-polyglot-5.8b-v2: direct link, hf CLI and curl.
- Browser
- Download file 210 Bytes
-
https://huggingface.co/nlpai-lab/kullm-polyglot-5.8b-v2/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://nlpai-lab/kullm-polyglot-5.8b-v2/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/nlpai-lab/kullm-polyglot-5.8b-v2/resolve/main/tokenizer_config.json
210 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|endoftext|>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<|endoftext|>", | |
| "tokenizer_class": "PreTrainedTokenizerFast" | |
| } | |