---
license: apache-2.0
base_model: Qwen/Qwen2-VL-2B-Instruct
library_name: mlx
language: en
tags:
  - mlx
  - vision-language
  - image-to-text
  - qwen2-vl
  - apple-silicon
pipeline_tag: image-text-to-text
---

# dam-vision-v3-mlx (8-bit)

MLX 8-bit build of the **DAM vision model** — a fine-tuned
[Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) with the
DAM **v3** LoRA merged into the base weights. This is the *same* fine-tuned model the
DAM pipeline serves via vLLM (tag `qwen2-vl-2b-v3+f2`), packaged here to run natively
on **Apple Silicon Macs with MLX** (Metal) — no server, no CUDA.

## Details

| | |
|---|---|
| Base | `Qwen/Qwen2-VL-2B-Instruct` |
| Fine-tune | DAM v3 LoRA, merged |
| Format | MLX, **8-bit** affine quantization (group size 64) |
| Size on disk | ~2.97 GB |
| Architecture | `Qwen2VLForConditionalGeneration` |
| Converter | `mlx-vlm` 0.6.2 (`mlx` 0.31.2) |

## Usage

```bash
pip install mlx-vlm
python -m mlx_vlm generate \
  --model Munchit/dam-vision-v3-mlx \
  --image your_frame.jpg \
  --prompt "Describe this image." \
  --max-tokens 128 --temperature 0.0
```

Python:

```python
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template

model, processor = load("Munchit/dam-vision-v3-mlx")
config = model.config
prompt = apply_chat_template(processor, config, "Describe this image.", num_images=1)
out = generate(model, processor, prompt, ["your_frame.jpg"], max_tokens=128, temp=0.0)
print(out)
```

## Known limitation (v3)

The v3 fine-tune has a known **caption-repetition** failure mode: a meaningful fraction
of outputs loop/repeat catastrophically. This is inherent to the v3 weights — it is not
introduced by the MLX conversion and is not fixable by decode settings or prompt changes;
it requires a v4 retrain. Keep this in mind before relying on it for production captioning.

## Provenance

Converted on Linux (MLX CPU build) from the merged v3 checkpoint; the MLX weight format is
portable and runs on Apple Silicon under Metal. Structural load + the underlying weights are
validated (the same model runs in the DAM vLLM pipeline); verify end-to-end generation on
an Apple Silicon device.