macadeliccc/Mistral-7B-v0.2-OpenHermes

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kPublished:Mar 25, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Cold

macadeliccc/Mistral-7B-v0.2-OpenHermes is a 7 billion parameter language model, fine-tuned from alpindale/Mistral-7B-v0.2 using the teknium/OpenHermes-2.5 dataset. Developed by macadeliccc, this model is proficient in Retrieval Augmented Generation (RAG) use cases, offering a specialized foundation for applications requiring enhanced factual grounding. It was trained efficiently in 13 hours on an A100 GPU, leveraging Unsloth and Huggingface's TRL library.

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Model Overview

macadeliccc/Mistral-7B-v0.2-OpenHermes is a 7 billion parameter language model, fine-tuned by macadeliccc from the alpindale/Mistral-7B-v0.2 base model. It was trained using the teknium/OpenHermes-2.5 dataset, with a focus on efficient training processes facilitated by Unsloth and Huggingface's TRL library, completing in 13 hours on an A100 GPU.

Key Capabilities & Features

  • RAG Proficiency: The model is specifically noted for its proficiency in Retrieval Augmented Generation (RAG) use cases, making it suitable for applications where external knowledge retrieval is crucial.
  • Efficient Training: Achieved rapid training times, indicating optimized fine-tuning techniques.
  • ChatML Prompt Template: Utilizes the ChatML format for conversational interactions, ensuring compatibility with common chat interfaces.
  • Quantization Support: Available in various quantized formats including GGUF, AWQ, HQQ-4bit, and ExLlamaV2 for optimized deployment across different hardware.

Performance Benchmarks

Evaluations conducted by Maxime Labonne show the model achieving an average score of 45.26% across several benchmarks:

  • AGIEval: 35.57%
  • GPT4All: 67.15%
  • TruthfulQA: 42.06%
  • Bigbench: 36.27%

Recommended Use Cases

  • RAG Applications: Ideal for scenarios requiring robust information retrieval and generation, with a recommendation for further RAG-specific fine-tuning.
  • Conversational AI: Suitable for building helpful assistant chatbots, leveraging its ChatML compatibility.
  • Resource-Constrained Environments: The availability of various quantizations makes it adaptable for deployment on hardware with limited resources.

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