Instructions to use LLM360/K2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM360/K2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LLM360/K2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LLM360/K2") model = AutoModelForCausalLM.from_pretrained("LLM360/K2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LLM360/K2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LLM360/K2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LLM360/K2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LLM360/K2
- SGLang
How to use LLM360/K2 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 "LLM360/K2" \ --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": "LLM360/K2", "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 "LLM360/K2" \ --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": "LLM360/K2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LLM360/K2 with Docker Model Runner:
docker model run hf.co/LLM360/K2
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<center><img src="k2_table_of_tables.png" alt="k2 big eval table"/></center>
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Detailed analysis can be found on the K2 Weights and Biases project [here](https://wandb.ai/llm360/K2?nw=29mu6l0zzqq)
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## K2 Gallery
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The K2 gallery allows one to browse the output of various prompts on intermediate K2 checkpoints, which provides an intuitive understanding on how the model develops and improves over time. This is inspired by The Bloom Book.
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<center><img src="k2_table_of_tables.png" alt="k2 big eval table"/></center>
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Detailed analysis can be found on the K2 Weights and Biases project [here](https://wandb.ai/llm360/K2?nw=29mu6l0zzqq)
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## Open LLM Leaderboard
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| Evaluation | Score | Raw Score |
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| ----------- | ----------- | ----------- |
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| IFEval | 22.52 | 23 |
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| BBH | 28.22 | 50 |
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| Math Lvl 5 | 2.04 | 2 |
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| GPQA | 3.58 | 28 |
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| MUSR | 8.55 | 40 |
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| MMLU-PRO | 22.27 | 30 |
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| Average | 14.53 | 35.17 |
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## K2 Gallery
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The K2 gallery allows one to browse the output of various prompts on intermediate K2 checkpoints, which provides an intuitive understanding on how the model develops and improves over time. This is inspired by The Bloom Book.
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