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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("LLM360/K2") model = AutoModelForMultimodalLM.from_pretrained("LLM360/K2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
Update README.md
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license: apache-2.0
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- nlp
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- llm
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# K2 - Deciphering Llama 2 70B
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K2 is a fully transparent large language model on par with Llama 2 - 70B.
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[insert table with comparison to llama 2 on open llm leaderboard metrics]
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[insert about the data mix]
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The first 10 checkpoints are available for download now.
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Checkpoint 360[link]
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Checkpoint 359[link]
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Checkpoint 358[link]
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Checkpoint 357[link]
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Checkpoint 356[link]
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Checkpoint 355[link]
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Checkpoint 354[link]
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Checkpoint 353[link]
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Checkpoint 352[link]
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Checkpoint 351[link]
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We are working on release caliber artifacts for the dataset, code, and analysis which will be released over the next few weeks.
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## 🟠 Model Description
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- **Model type:** Language model with the same architecture as LLaMA-7B
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0
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- **Resources for more information:**
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- [Training Code]
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- [Data Preparation]
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- [Metrics]
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- [Fully processed Amber pretraining data]
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## About LLM360
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LLM360 is an initiative for comprehensive and fully open-sourced LLMs,
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where all training details, model checkpoints, intermediate results, and
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additional analyses are made available to the community. Our goal is to advance
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the field by inviting the community to deepen the understanding of LLMs
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together. As the first step of the project LLM360, we release all intermediate
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model checkpoints, our fully-prepared pre-training dataset, all source code and
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configurations, and training details. We are
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committed to continually pushing the boundaries of LLMs through this open-source
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effort.
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[Visit us](https://www.llm360.ai/)
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