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Docmatix-Qwen2-VL-7B-Instruct-finetuned – AI Model by S-ABISHEAK | AlphaNeural AI

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S-ABISHEAK/
Docmatix-Qwen2-VL-7B-Instruct-finetuned

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HuggingFaceM4/Docmatix

unsloth/Qwen2-VL-7B-Instruct

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Qwen2-VL-7B-Instruct Fine-tuned on Docmatix

This model is a fine-tuned version of unsloth/Qwen2-VL-7B-Instruct using Unsloth and the HuggingFaceM4/Docmatix dataset.

Model Details

Developed by: [Your Name/Organization]
Model type: Multimodal Large Language Model (Vision-Language)
Language(s): English
Finetuned from model: unsloth/Qwen2-VL-7B-Instruct
Finetuning approach: LoRA (Low-Rank Adaptation)

Training Details

Training Data

Fine-tuned on the images split of the Docmatix dataset, which focuses on document understanding and visual question answering.

Training Hyperparameters

Method: SFT (Supervised Fine-Tuning) with LoRA
LoRA Rank (r): 8
LoRA Alpha: 16
Optimizer: AdamW (8-bit)
Learning Rate: 1e-4
Batch Size: 1 (with Gradient Accumulation steps: 8)
Max Steps: 200
Precision: fp16/bf16 (depending on hardware compatibility)

How to Get Started with the Model

Loading the LoRA Adapter

python

from unsloth import FastVisionModel
import torch

model, tokenizer = FastVisionModel.from_pretrained(
    "unsloth/Qwen2-VL-7B-Instruct",
    load_in_4bit=True,
)

model = FastVisionModel.load_adapter(model, "path_to_your_lora_files")

Inference Example

python

from transformers import TextStreamer

FastVisionModel.for_inference(model)

# Standard Qwen2-VL inference code follows...

Framework versions

PEFT 0.19.1
Unsloth 2026.5.8
Transformers 5.0.0
PyTorch 2.11.0