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 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
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:
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.