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issai/
Qolda-AVL-5B

Name: Qolda-AVL-5B
Availability: InStock

safetensors

qwen3_vl

custom_code

Qwen/Qwen3-VL-4B-Thinking

finetune

apache-2.0

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Qolda-AVL

Qolda-AVL is a 5B audio-vision-language model designed to operate in Kazakh, Russian, and English. The model extends Qwen3-VL with an audio branch built on a fine-tuned Whisper encoder and a dedicated audio projection module. All three modalities are adapted to Kazakh through a staged training pipeline, with the audio branch covering speech recognition, speech translation, audio classification, and environmental sound captioning.

To improve audio feature injection into the language backbone, we apply the DeepStack mechanism to the audio branch, mirroring the vision processing pipeline of Qwen3-VL 💜

The model is our step towards omni-modal systems for the Kazakh language.

The name "Qolda" reflects both its design and purpose in Kazakh: "in hand" (қолда) for its compact accessibility, and "to support" (қолдау) for its assistive nature.

Evaluation

The benchmark suites and Qolda-AVL model collection are available here:

Text benchmarks: https://huggingface.co/collections/issai/qolda-language-benchmarks
Vision benchmarks: https://huggingface.co/collections/issai/qolda-vision-benchmarks
Audio benchmarks: https://huggingface.co/collections/issai/qolda-avl-audio-benchmarks
Qolda model: https://huggingface.co/issai/Qolda

The following tables report benchmark results across the Qolda-AVL family together with Qolda baselines. Higher is better unless otherwise noted, and the best result within each row is shown in bold.

5B / 9B / 34B = Qolda-AVL variants · Qolda-NT = Qolda No-Think · Qolda-T = Qolda Think

Click to for Text benchmark results

Benchmark	Language	Qolda-AVL-5B	Qolda-AVL-9B	Qolda-AVL-34B	Qolda-NT	Qolda-T
MMLU	Kazakh	73.07	73.89	81.94	58.39	69.28
	English	79.09	80.96	86.71	70.03	76.46
MMLU-Pro	Kazakh	62.21	64.28	73.67	40.62	57.68
	English	71.26	72.61	79.00	58.09	66.31
	Russian	66.79	68.29	76.34	47.42	62.45
GPQA	Kazakh	47.91	46.97	58.77	31.89	38.60
	English	52.68	53.36	63.81	39.46	45.62
	Russian	48.78	51.34	59.36	32.60	40.19
ARC	Kazakh	93.59	94.27	96.76	86.14	92.30
	English	96.90	97.29	98.17	94.22	96.11
	Russian	96.11	96.62	97.63	91.17	94.17
GSM8K	Kazakh	85.75	83.17	90.52	73.01	83.00
	English	95.22	95.83	96.44	62.85	90.22
	Russian	90.90	92.04	94.31	84.99	83.98
MMLU-Redux	Kazakh	76.24	76.92	84.39	60.06	72.38
	English	82.65	84.56	88.11	72.91	79.40
KazCulture	Kazakh	44.75	56.39	62.37	53.00	47.45
KazMMLU	Kazakh	69.27	73.04	78.98	58.11	66.14
KazBench	Kazakh	61.83	64.61	70.05	64.23	61.12
Belebele	Kazakh	81.70	84.76	88.78	81.07	82.91
PIQA	Kazakh	81.00	78.00	85.00	63.00	70.00
INCLUDE	Kazakh	53.80	57.20	61.80	45.20	46.00
	Russian	58.15	64.49	70.83	59.17	56.52
KKCOPA	Kazakh	76.60	78.11	79.60	70.00	73.79
NIS Math	Kazakh	94.00	93.00	98.00	66.00	87.88
KazQAD	Kazakh	42.28	65.76	70.36	70.99	67.40
RAGBench	Kazakh	52.12	62.91	69.98	54.95	66.81

Click to for Vision benchmark results

Benchmark	Language	Qolda-AVL-5B	Qolda-AVL-9B	Qolda-AVL-34B	Qolda-NT	Qolda-T
RealWorldQA	Kazakh	52.81	50.07	55.42	53.86	48.10
	English	67.84	70.07	71.76	61.57	61.70
	Russian	59.35	60.39	66.41	56.08	57.12
MMStar	Kazakh	67.45	68.89	72.50	53.08	59.60
	English	70.27	72.93	75.93	58.48	65.04
	Russian	66.47	69.45	73.93	55.48	59.84
AI2D	Kazakh	72.18	73.34	79.05	63.48	66.26
	English	79.40	81.96	84.55	73.99	75.61
MathVista	Kazakh	70.17	72.34	74.65	58.32	66.33
	English	76.75	80.94	82.57	63.14	71.04
MathVision	Kazakh	52.07	55.75	62.06	35.41	44.38
	English	54.93	58.24	63.90	42.00	48.05
MMBench	Kazakh	87.69	89.19	90.18	79.97	83.85
	English	87.89	89.01	90.43	83.05	84.40
OCRBench	Kazakh	30.61	51.25	53.97	49.89	46.49
	English	73.20	77.20	79.20	69.90	68.70

Click to for Audio benchmark results

Benchmark	Language	Qolda-AVL-5B	Qolda-AVL-9B	Qolda-AVL-34B
SAKURA · Animal · Multi	Kazakh	48.58	69.54	70.00
	English	64.60	82.20	83.00
	Russian	50.40	81.76	83.00
SAKURA · Animal · Single	Kazakh	55.31	87.80	86.00
	English	52.20	87.80	83.80
	Russian	53.23	88.40	88.20
SAKURA · Emotion · Multi	Kazakh	33.87	36.95	39.20
	English	35.80	37.40	43.20
	Russian	38.20	37.20	40.20
SAKURA · Emotion · Single	Kazakh	39.03	47.27	45.58
	English	42.60	44.40	47.40
	Russian	40.28	44.80	47.60
SAKURA · Gender · Multi	Kazakh	67.94	75.40	81.60
	English	72.00	83.20	82.80
	Russian	70.42	77.60	83.20
SAKURA · Gender · Single	Kazakh	70.88	82.80	84.20
	English	80.00	85.00	87.80
	Russian	76.92	82.60	87.40
SAKURA · Language · Multi	Kazakh	87.40	88.80	92.60
	English	92.60	92.00	94.40
	Russian	87.80	90.80	93.60
SAKURA · Language · Single	Kazakh	96.00	97.60	97.40
	English	96.40	97.40	97.40
	Russian	97.19	97.60	97.60
SpokenMQA · Long Digit	Kazakh	81.40	88.95	91.28
	English	88.37	93.02	94.19
SpokenMQA · Multi-step Reasoning	Kazakh	74.57	77.66	87.28
	English	87.42	90.23	92.53
SpokenMQA · Short Digit	Kazakh	84.00	92.00	95.00
	English	88.00	94.00	92.00
SpokenMQA · Single-step Reasoning	Kazakh	90.20	92.74	93.92
	English	93.92	95.10	94.76
ASR · WER ↓	Kazakh	0.1707	0.1452	0.1688
	English	0.0662	0.0634	0.0601
	Russian	0.1136	0.1095	0.1077
WavCaps	Kazakh	0.64	6.24	6.53
	English	3.82	14.28	15.03
	Russian	1.27	8.38	8.32
WavCaps-QA	Kazakh	8.55	25.33	25.33
	English	24.34	35.86	34.21
	Russian	19.08	32.24	31.25

Model Usage

1. `Transformers` inference

To run the inference with transformers, complete the preliminary setup:

bash

uv venv venv
source venv/bin/activate
uv pip install torch accelerate transformers

Then initialize the model and processor:

python

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

model = AutoModelForCausalLM.from_pretrained(
    "issai/Qolda-AVL-5B",
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map="auto"
)

processor = AutoProcessor.from_pretrained("issai/Qolda-AVL-5B", trust_remote_code=True)

Depending on the required modalities, define the messages list:

Language:

python

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "y = (lnx)^2 функциясының туындысын тап. JSON форматында жауап бер: {'answer': '...'}"},
        ],
    }
]

Vision-Language:

python

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "assets/sample_image.jpg"}, # or provide link to the image
            {"type": "text", "text": "Суретті егжей-тегжейлі сипаттап бер. Неше жылқы көріп тұрсың және олардың түстері қандай?"},
        ],
    }
]

Audio-Language:

Note: The model was not trained to answer questions posed directly in the audio. Provide a detailed text instruction alongside the audio describing the task you want performed on it.

python

prompt = """Математикалық есепті шеш.
Respond ONLY with this JSON format: {"explanation": "<your step-by-step reasoning>", "answer": <integer or float number>}
The answer must be a number (integer or float). No text, no units, just the number.
"""

messages = [
    {
        "role": "user",
        "content": [
            {"type": "audio", "audio": "assets/sample_audio.wav"}, # or provide link to the audio
            {"type": "text", "text": prompt}
        ],
    }
]

Audio-Vision-Language:

python

messages = [
    {
        "role": "user",
        "content": [
            {"type": "audio", "audio": "assets/question_audio.wav"},
            {"type": "image", "image": "assets/sample_image.jpg"},
            {"type": "text", "text": "Answer the question"},
        ],
    }
]

Finally, pass the messages to the model for inference:

python

inputs = processor.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True,
    return_dict=True, return_tensors="pt",
).to(model.device)

generated_ids = model.generate(
    **inputs,
    max_new_tokens=4096,
    temperature=0.7,
    top_p=0.95,
    top_k=20,
    do_sample=True,
    repetition_penalty=1.0,
)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True)

print(processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True)[0])

2. `vLLM` inference

Alternatively, you can run the model via a vLLM server. Note that we use a custom vLLM package. First, complete the preliminary setup:

bash

uv venv venv
source venv/bin/activate

# Install this fork (precompiled binaries)
git clone https://github.com/IS2AI/vLLM-Qolda-AVL.git
cd vLLM-Qolda-AVL
VLLM_USE_PRECOMPILED=1 uv pip install -e .

Then start the OpenAI-compatible server (adjust parameters to your settings):

bash

vllm serve issai/Qolda-AVL-5B \
    --served-model-name qolda-avl \
    --trust-remote-code \
    --tensor-parallel-size 4 \
    --dtype bfloat16 \
    --max-model-len 16384 \
    --limit-mm-per-prompt '{"audio": 1, "image": 1}'

To run inference, you can use the following code:

python

import base64
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1", 
    api_key="EMPTY"
)

def encode_audio_base64(path: str | Path) -> str:
    with open(path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")

def encode_image_base64(path: str | Path) -> str:
    with open(path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")

audio_path = "assets/sample_audio.wav"
audio_b64 = encode_audio_base64(audio_path)

stream = client.chat.completions.create(
    model=client.models.list().data[0].id,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_audio",
                    "input_audio": {
                        "data": audio_b64,
                        "format": "wav",
                    },
                },
                {
                    "type": "text",
                    "text": (
                        "Analyze the voice in the audio and identify the speaker's "
                        "gender (male or female). Also transcribe what is said. "
                        "Return your answer as JSON in the following format: "
                        '{"answer": "<male or female>",'
                        '"transcription": "<transcription>"}'
                    ),
                },
            ],
        }
    ],
    max_tokens=4096,
    temperature=0.7,
    top_p=0.8,
    stream=True,
    stream_options={"include_usage": True},
)

text = ""
usage = None
for chunk in stream:
    if chunk.usage:
        usage = chunk.usage
    if chunk.choices and chunk.choices[0].delta.content:
        token = chunk.choices[0].delta.content
        print(token, end="", flush=True)
        text += token

License

Apache License 2.0

Citation

bibtex

@article{qolda-avl-bdcc,
AUTHOR = {Arystanbekov, Batyr and Maxutov, Akylbek and Nurimanov, Aspandiyar and Varol, Huseyin Atakan},
TITLE = {Extending a Vision–Language Model with Audio Understanding: Introducing Qolda-AVL for the Kazakh Language},
JOURNAL = {Big Data and Cognitive Computing},
VOLUME = {10},
YEAR = {2026},
NUMBER = {6},
ARTICLE-NUMBER = {192},
URL = {https://www.mdpi.com/2504-2289/10/6/192},
ISSN = {2504-2289},
DOI = {10.3390/bdcc10060192}
}

issai/Qolda-AVL-5B

Qolda-AVL

Evaluation

Model Usage

1. Transformers inference

Language:

Vision-Language:

Audio-Language:

Audio-Vision-Language:

2. vLLM inference

License

Citation

issai/
Qolda-AVL-5B

1. `Transformers` inference

2. `vLLM` inference