alt.freeweights.qwen / Aquila-VL-2B-llava-qwen
From: BAAI <huggingface.co/BAAI>Newsgroups: alt.freeweights.qwenSubject:

Aquila-VL-2B-llava-qwen

Date: 2026-11-15Commit: 32a63cfSigned: 2DC28CC7B26A283B ✓

Visual question answering model with 2.2B parameters. Safetensors weights, 4.37 GB in all. Published by BAAI under Apache-2.0.

2.2B params4.37GBAquila-VL-2B-llava-qwensafetensorspermissive
FactsParams2.2BSize4.37 GBFormatssafetensorsTaskvisual question answeringLicenseApache-2.0From sourcepermissive
$ fw get BAAI/Aquila-VL-2B-llava-qwen
# manifest sha256 cb9367e7b0dd… · 4.37 GB · 11 files
$ fw verify ./Aquila-VL-2B-llava-qwen BAAI/Aquila-VL-2B-llava-qwen
From sourceHosted (signed)BAAI/Aquila-VL-2B-llava-qwenLast check: 2026-11-15 03:57 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Fine-tune of Qwen2.5-1.5B-Instruct.

Works with

Runs with

Ranked by fit to this model (safetensors), then by GitHub stars.

Overview

Size
4.37 GB
Weights
4.36 GB (safetensors)
Parameters
2.2B
Formats
safetensors
Task
visual question answering
License
Apache-2.0 (permissive)
Files
11, each with a sha256
Publisher
BAAI
Added
2026-11-15
Section
alt.freeweights.qwen
Lineage

Original release from the publisher.

Base model: Qwen/Qwen2.5-1.5B-Instruct

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Publisher at the pinned commit→You

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From sourcehuggingface.co2026-11-15 03:57 UTCpassed

Model card

From the publisher at commit 32a63cf. Shown as plain text with links; images open at the publisher.

Image: mof-class1

Introduction#

The Aquila-VL-2B model is a vision-language model (VLM) trained based on the LLava-one-vision framework. The Qwen2.5-1.5B-instruct model is chose as the LLM, while siglip-so400m-patch14-384 is utilized as the vision tower.

The model was trained on our self-built Infinity-MM dataset, which contains approximately 40 million image-text pairs. This dataset is a combination of open-source data collected from the internet and synthetic instruction data generated using open-source VLM models.

We have open-sourced Infinity-MM dataset and related resources. We hope you enjoy using them!

News#

Evaluation#

We evaluated the model using the VLMEvalKit tool. Whenever possible, we prioritized using the OpenAI API for test sets that support API-based evaluation.

BenchmarkMiniCPM-V-2InternVL2-2BXinYuan-VL-2BQwen2-VL-2B-InstructAquila-VL-2B
MMBench-ENtest69.473.478.974.978.8
MMBench-CNtest65.970.976.173.976.4
MMBench_V1.1test65.269.775.472.775.2
MMT-Benchtest54.553.357.254.858.2
RealWorldQA55.457.363.962.663.9
HallusionBench36.838.136.041.543.0
SEEDBench2plus51.860.063.062.463.0
LLaVABench66.164.842.452.568.4
MMStar41.650.251.947.854.9
POPE86.685.389.488.083.6
MMVet44.041.142.750.744.3
MMMUval39.634.943.641.747.4
ScienceQAtest80.494.186.678.195.2
AI2Dtest64.874.474.274.675.0
MathVistatestmini39.045.047.147.959.0
MathVersetestmini19.824.722.221.026.2
MathVision15.412.616.317.518.4
DocVQAtest71.086.987.689.985.0
InfoVQAtest40.059.559.165.458.3
ChartQAtest59.671.457.173.576.5
TextVQAval74.373.577.679.976.4
OCRVQAtestcore54.440.267.668.764.0
VCRen easy27.651.667.768.370.0
OCRBench613784782810772
Average53.558.860.962.164.1

For comparison models, evaluations were conducted in a local environment, so the scores may differ slightly from those reported in papers or on the official VLMEvalKit leaderboard.

How to use#

# pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
from llava.model.builder import load_pretrained_model
from llava.mm_utils import process_images, tokenizer_image_token
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from llava.conversation import conv_templates
from PIL import Image
import requests
import copy
import torch
import warnings

warnings.filterwarnings("ignore")

pretrained = "BAAI/Aquila-VL-2B-llava-qwen"

model_name = "llava_qwen"
device = "cuda"
device_map = "auto"
tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map)  # Add any other thing you want to pass in llava_model_args

model.eval()

# load image from url
url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
image = Image.open(requests.get(url, stream=True).raw)

# load image from local environment
# url = "./local_image.jpg"
# image = Image.open(url)

image_tensor = process_images([image], image_processor, model.config)
image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]

conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
conv = copy.deepcopy(conv_templates[conv_template])
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()

input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
image_sizes = [image.size]

cont = model.generate(
    input_ids,
    images=image_tensor,
    image_sizes=image_sizes,
    do_sample=False,
    temperature=0,
    max_new_tokens=4096,
)

text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)

print(text_outputs)

Future Plan#

  • We plan to train models of various sizes.
  • Future training will incorporate multi-image and video data.

Citation#

If you find this useful, please cite the following work

@misc{gu2024infinitymmscalingmultimodalperformance,
      title={Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data}, 
      author={Shuhao Gu and Jialing Zhang and Siyuan Zhou and Kevin Yu and Zhaohu Xing and Liangdong Wang and Zhou Cao and Jintao Jia and Zhuoyi Zhang and Yixuan Wang and Zhenchong Hu and Bo-Wen Zhang and Jijie Li and Dong Liang and Yingli Zhao and Yulong Ao and Yaoqi Liu and Fangxiang Feng and Guang Liu},
      year={2024},
      eprint={2410.18558},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.18558}, 
}

Acknowledgements#

This work is supported by the National Science and Technology Major Project (No. 2022ZD0116300). 本项目受新一代人工智能国家科技重大专项(No. 2022ZD0116300)支持。

Files

11 files · 4.37 GBWeights 4.36 GB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 11 files with their sha256
FileSizesha256
.gitattributes1.52 KB11ad7efa24975ee4b0c3c3a38ed18737f0658a5f75a0a96787b576a78a023361
README.md8.22 KB09af365d00f038cee047c29220f9f4fbaef6da62762f10b6cbabf4cfba0d8776
added_tokens.json605 B58b54bbe36fc752f79a24a271ef66a0a0830054b4dfad94bde757d851968060b
config.json2.83 KB87f43e725f63e0ef6ba0a8a4fecc0029037b5eb4b692573e8ecab57552dc089c
generation_config.json242 B25481c01273e41e2ddb17db14fe6872073fd6db27409780e7afdf518252f3716
merges.txt1.67 MB8831e4f1a044471340f7c0a83d7bd71306a5b867e95fd870f74d0c5308a904d5
model.safetensors4.36 GBbee90b68ebc82024152c2b6182d914dd63383e73f21b02e1fcb576e41b45962e
special_tokens_map.json613 B76862e765266b85aa9459767e33cbaf13970f327a0e88d1c65846c2ddd3a1ecd
tokenizer.json7.03 MBa8506e7111b80c6d8635951a02eab0f4e1a8e4e5772da83846579e97b16f61bf
tokenizer_config.json7.33 KB62bc3af9dedb61f5417cc2790298074ceae0394655743dd5a2013625c7ded585
vocab.json2.78 MBca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
32a63cf22d pinned now2026-11-15 03:57 UTC11 files · 4.37 GB

Use it

$ fw get BAAI/Aquila-VL-2B-llava-qwen
# manifest sha256 cb9367e7b0dd… · 4.37 GB · 11 files
$ fw verify ./Aquila-VL-2B-llava-qwen BAAI/Aquila-VL-2B-llava-qwen
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/BAAI/Aquila-VL-2B-llava-qwen.json --dir ./Aquila-VL-2B-llava-qwen $ python3 fw.py verify ./Aquila-VL-2B-llava-qwen ./BAAI__Aquila-VL-2B-llava-qwen.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download BAAI/Aquila-VL-2B-llava-qwen --revision 32a63cf22d73a5f253d994987fa96e0bf5515e15 --local-dir ./Aquila-VL-2B-llava-qwen

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
09af365d00f038cee047c29220f9f4fbaef6da62762f10b6cbabf4cfba0d8776
Terms gate
No extra terms beyond the license.

Read the license text

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Provenance

From: BAAI <huggingface.co/BAAI>Newsgroups: alt.freeweights.qwenSubject: Aquila-VL-2B-llava-qwenDate: 2026-11-15Commit: 32a63cfSigned: 2DC28CC7B26A283B ✓
Upstream
BAAI/Aquila-VL-2B-llava-qwen
Pinned commit
32a63cf22d73a5f253d994987fa96e0bf5515e15
Manifest
Aquila-VL-2B-llava-qwen.json · .minisig
Manifest sha256
cb9367e7b0ddee188e980d199081a439519cb9d5fac9525fe050e7ca7881bc22
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 03:57 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/BAAI/Aquila-VL-2B-llava-qwen.json $ curl -O https://freeweights.allternit.com/manifests/BAAI/Aquila-VL-2B-llava-qwen.json.minisig $ minisign -Vm Aquila-VL-2B-llava-qwen.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

Parent

Qwen/Qwen2.5-1.5B-Instruct · this model is a finetune of it

No model in the catalog names this one as its parent.

Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download BAAI/Aquila-VL-2B-llava-qwen
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. Aquila-VL-2B-llava-qwen.json. Provenance. Takedown history: none.
From: Perciqa <huggingface.co/Perciqa>Subject: Aurora-Code-1
From: BAAI <huggingface.co/BAAI>Subject: bge-reasoner-embed-qwen3-8b-0923
From: amazon <huggingface.co/amazon>Subject: BMOJOF-primed-HQwen3-8B-Instruct
From: nbeerbower <huggingface.co/nbeerbower>Subject: Bubba-Qwen3.6-27B-LoRA
From: bartowski <huggingface.co/bartowski>Subject: bytkim_Qwen3.8-27B-pi-GGUF
From: mlx-community <huggingface.co/mlx-community>Subject: CLM-v0.1-8B-MLX-8bit