Satquest Qwen2vl Sft v2 Merged
Qwen2-VL-2B fine-tuned for satellite imagery question answering — produces complete natural-language sentences.
Source du modèle
- Type
- Other
- Taille du fichier
- 4,11 GB
- Modèle de base
- Abdullah-123/qwen2vl-2b-hrvqa-lora
- Version
- vRevision ac49695
- Mis à jour
- 10 oct. 2026
- Téléchargements
- 11
- Source
- Hugging Face
Description de la source
🛰️ SatQuest — Satellite VQA (Continued SFT v2)
Qwen2-VL-2B fine-tuned for satellite imagery question answering — produces complete natural-language sentences.
Built on top of Abdullah-123/qwen2vl-2b-hrvqa-lora with continued SFT to teach sentence-style responses.
Before vs After
Q: What is the main transportation? → Before: "car" | After: "The main mode of transportation visible in the image is car."
Q: Is there any water section? → Before: "yes" | After: "Yes, there is a water section present in the image."
Q: How many small vehicles? → Before: "2" | After: "There are two small vehicles visible in the image."
Model Details
| Property | Value |
|---|---|
| Base | Qwen2-VL-2B-Instruct |
| First fine-tune | HRVQA dataset — 159,980 QA pairs |
| This fine-tune | 20,000 verbalized sentence-style QA pairs |
| Method | LoRA (rank=16) via Unsloth + TRL SFTTrainer |
| GPU | NVIDIA A100 80GB |
| Epochs | 2 (LR=1e-4, cosine) |
Usage (with Unsloth)
from unsloth import FastVisionModel
model, tokenizer = FastVisionModel.from_pretrained(
'Humaix/satquest-qwen2vl-sft-v2',
load_in_4bit=True,
)
FastVisionModel.for_inference(model)
Usage — Merged Model (plain transformers, for production backend)
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
import torch
model = Qwen2VLForConditionalGeneration.from_pretrained(
'Humaix/satquest-qwen2vl-sft-v2-merged',
torch_dtype=torch.float16,
device_map='auto',
)
processor = AutoProcessor.from_pretrained('Humaix/satquest-qwen2vl-sft-v2-merged')
Project
NED University of Engineering & Technology
Final Year Design Project (CS-22030) — SatQuest: AI-powered Satellite Imagery Question Answering System
Sources
1 source
Sources
1 sourceVérifié 10 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- language: - en license: apache-2.0 base_model: Abdullah-123/qwen2vl-2b-hrvqa-lora tags: - satellite-imagery - visual-question-answering - qwen2-vl - fine-tuned - unsloth - lora datasets: - JNIC1/HRVQA --- # 🛰️ SatQuest — Satellite VQA (Continued SFT v2) **Qwen2-VL-2B fine-tuned for satellite imagery question answering — produces complete natural-language sentences.** Built on top of [Abdullah-123/qwen2vl-2b-hrvqa-lora](https://huggingface.co/Abdullah-123/qwen2vl-2b-hrvqa-lora) with continued SFT to teach sentence-style responses. ## Before vs After ``` Q: What is the main transportation? → Before: "car" | After: "The main mode of transportation visible in the image is car." Q: Is there any water section? → Before: "yes" | After: "Yes, there is a water section present in the image." Q: How many small vehicles? → Before: "2" | After: "There are two small vehicles visible in the image." ``` ## Model Details | Property | Value | |----------|-------| | Base | Qwen2-VL-2B-Instruct | | First fine-tune | HRVQA dataset — 159,980 QA pairs | | This fine-tune | 20,000 verbalized sentence-style QA pairs | | Method | LoRA (rank=16) via Unsloth + TRL SFTTrainer | | GPU | NVIDIA A100 80GB | | Epochs | 2 (LR=1e-4, cosine) | ## Usage (with Unsloth) ```python from unsloth import FastVisionModel model, tokenizer = FastVisionModel.from_pretrained( 'Humaix/satquest-qwen2vl-sft-v2', load_in_4bit=True, ) FastVisionModel.for_inference(model) ``` ## Usage — Merged Model (plain transformers, for production backend) ```python from transformers import Qwen2VLForConditionalGeneration, AutoProcessor import torch model = Qwen2VLForConditionalGeneration.from_pretrained( 'Humaix/satquest-qwen2vl-sft-v2-merged', torch_dtype=torch.float16, device_map='auto', ) processor = AutoProcessor.from_pretrained('Humaix/satquest-qwen2vl-sft-v2-merged') ``` ## Project **NED University of Engineering & Technology** Final Year Design Project (CS-22030) — SatQuest: AI-powered Satellite Imagery Question Answering System
Source context: 11 downloads · 1 likes · Pipeline visual-question-answering · Repo Humaix/satquest-qwen2vl-sft-v2-merged
Source context: 5 downloads · 1 likes · Pipeline visual-question-answering · Repo Humaix/satquest-qwen2vl-sft-v2-merged
