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
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Download README.md from nlpai-lab/kullm-polyglot-5.8b-v2: direct link, hf CLI and curl.
- Browser
- Download file 821 Bytes
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https://huggingface.co/nlpai-lab/kullm-polyglot-5.8b-v2/resolve/main/README.md
- Command line
-
hf download hf://nlpai-lab/kullm-polyglot-5.8b-v2/README.md
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curl -L -o README.md https://huggingface.co/nlpai-lab/kullm-polyglot-5.8b-v2/resolve/main/README.md
821 Bytes
| license: apache-2.0 | |
| datasets: | |
| - nlpai-lab/kullm-v2 | |
| language: | |
| - ko | |
| # KULLM-Polyglot-5.8B-v2 | |
| This model is a parameter-efficient fine-tuned version of [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b) on a KULLM v2 | |
| Detail Codes are available at [KULLM Github Repository](https://github.com/nlpai-lab/KULLM) | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 3e-4 | |
| - train_batch_size: 128 | |
| - seed: 42 | |
| - distributed_type: multi-GPU (A100 80G) | |
| - num_devices: 4 | |
| - gradient_accumulation_steps: 8 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 8.0 | |
| ### Framework versions | |
| - Transformers 4.28.1 | |
| - Pytorch 2.0.0+cu117 | |
| - Datasets 2.11.0 | |
| - Tokenizers 0.13.3 |