Buddhi-128K-Chat
# Buddhi-128K-Chat (7B) vLLM Inference: [](https://colab.research.google.com/drive/11_8W8FpKK-856QdRVJLyzbu9g-DMxNfg?usp=sharing) # Read release article: [🔗 Introducing Buddhi: Open-Source Chat Model with a 128K Context Window 🔗 ](https://medium.aiplanet.com/introducing-buddhi-open-source-chat-model-with-a-128k-context-window-06a1848121d0)  ## Model Description Buddhi-128k-Chat is a general-purpose first chat model with 128K context length window. It is meticulously fine-tuned on the Mistral 7B Instruct, and optimised to handle an extended context length of up to 128,000 tokens using the innovative YaRN (Yet another Rope Extension) Technique. This enhancement allows Buddhi to maintain a deeper understanding of context in long documents or conversations, making it particularly adept at tasks requiring extensive context retention, such as comprehensive document summarization, detailed narrative generation, and intricate question-answering. ## Architecture The Buddhi-128K-Chat model is fine-tuned on the Mistral-7B Instruct base model. We selected the Mistral 7B Instruct v0.2 as the parent model due to its superior reasoning capabilities. The architecture of the Mistral-7B model includes features like Grouped-Query Attention and Byte-fallback BPE tokenizer. Originally, this model has 32,768 maximum position embeddings. To increase the context size to 128K, we needed to modify the positional embeddings, which is where YaRN comes into play. In our approach, we utilized the NTK-aware technique, which recommends alternative interpolation techniques for positional interpolation. One experimentation involved Dynamic-YARN, suggesting the dynamic value of the 's' scale factor. This is because during inference, the sequence length changes by 1 after every word prediction. By integrating these position embeddings with the Mistral-7B Instruct base model, we achieved the 128K model. Additionally, we fine-tuned the model on our dataset to contribute one of the very few 128K chat-based models available in the open-source community with greater reasoning capabilities than all of it. ### Hardware requirements: > For 128k Context Length > - 80GB VRAM - A100 Preferred > For 32k Context Length > - 40GB VRAM - A100 Preferred ### vLLM - For Faster Inference #### Installation ``` !pip install vllm !pip install flash_attn # If Flash Attention 2 is supported by your System ``` Please check out [Flash Attention 2](https://github.com/Dao-AILab/flash-attention) Github Repository for more instructions on how to Install it. **Implementation**: > Note: The actual hardware requirements to run the model is roughly around 70GB VRAM. For experimentation, we are limiting the context length to 75K instead of 128K. This make it suitable for testing the model in 30-35 GB VRAM ```python from vllm import LLM, SamplingParams llm = LLM( model='aiplanet/buddhi-128k-chat-7b', trust_remote_code=True, dtype = 'bfloat16', gpu_memory_utilization=1, max_model_len= 75000 ) prompts = [ """