InferenceIllusionist/Mistral-RealworldQA-v0.2-7b-SFT
Mistral-RealworldQA-v0.2-7b SFT
<img src="https://i.imgur.com/Pf53ms5.jpeg" width="400"/>
GGUFs can be found here
An experiment with the goal of reducing hallucinations in VQA
First in a series of experiments centering around fine-tuning for image captioning.
<h1>Release Notes</h1>
- v0.1 - Initial Release
- <b>v0.2</b> (Current)- Updating base model to official Mistral-7b fp16 release, refinements to dataset and instruction formating
<h2>Background & Methodology</h2>
Mistral-7b-02 base model was fine-tuned using the RealWorldQA dataset, originally provided by the X.Ai Team here: https://x.ai/blog/grok-1.5v
<h1>Vision Results</h1>
Example 1 <img src="https://i.imgur.com/E9mS4Xb.jpeg" width="400"/> Example 2 <img src="https://i.imgur.com/SmTz1Yd.jpeg" width="400"/>
- Experiment yielded model that provides shorter, less verbose output for questions about pictures
- The likelihood of hallucinations in output has decreased, however, the model can still be easily influenced to be inaccurate by the user
- Best suited for captioning use cases that require concise descriptions and low token counts
- This model lacks the conversational prose of Excalibur-7b-DPO and is much "drier" in tone
<b>Requires additional mmproj file. You have two options for vision functionality (available inside this repo):</b>
Select the gguf file of your choice in Koboldcpp as usual, then make sure to choose the mmproj file above in the LLaVA mmproj field of the model submenu: <img src="https://i.imgur.com/x8vqH29.png" width="425"/>
Prompt Format
Use Alpaca for best results.
Other info
- Developed by: InferenceIllusionist
- License: apache-2.0
- Finetuned from model : mistral-community/Mistral-7B-v0.2
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.