---
library_name: transformers
pipeline_tag: image-text-to-text
base_model:
- LiquidAI/LFM3-VL-2B
---

This is a randomly initialized tiny model based on [LiquidAI/LFM3-VL-2B](https://huggingface.co/LiquidAI/LFM3-VL-2B) for testing and debugging.

| Feature | Source | This repo |
|---|---|---|
| Checkpoint tensor elements | 1.92B BF16 | 13.81M BF16 |
| Safetensors weight files | 3.84 GB (model.safetensors) | 27.64 MB (model.safetensors) |
| Text layers / layer-type cycle | 24 / two-conv prefix, then full/conv/sliding/conv | 10 / same prefix and two complete cycles |
| Text hidden width / MLP width | 2,048 / 7,680 | 64 / 128 |
| Text Q heads / KV heads / head dim | 16 / 4 / 128 | 16 / 4 / 32 |
| Vision layers / layer-type cycle | 14 / two-conv prefix, then six full/conv pairs | 6 / same prefix and two complete full/conv cycles |
| Vision hidden width / MLP width | 1,024 / 2,560 | 256 / 640 |
| Vision Q heads / KV heads / head dim | 16 / 8 / 64 | 8 / 4 / 32 |
| Patch / temporal patch / spatial merge | 16 / 2 / 2 | Same |
| Vision depthwise conv kernel | 3 x 3 | Same |
| RoPE | vision axial theta 1e6; text full/sliding dual theta | Same |
| Projector MLP hidden / downsample factor | 4,096 / 2 | 128 / 2 |
| Sandwich norms / QK RMSNorm / sigmoid head gate | present | Same |
| Vocabulary rows / embedding tying | 128,000 / tied | Same |
| Text maximum context length | 128,000 | Same |
| Stored precision | BF16 | Same |

Text-backbone widths follow [tiny-random/lfm3](https://huggingface.co/tiny-random/lfm3), which follows [tiny-random/lfm2](https://huggingface.co/tiny-random/lfm2). The pinned upstream implementation is included for Transformers loading. This repo provides a Transformers example with image and video inputs; no serving-backend validation is claimed.

| File path | Size |
|------|------|
| model.safetensors | 27.6MB |

### Example usage

#### Transformers (✅ verified)

Load the included pinned upstream implementation with `trust_remote_code=True`. The example sends one image and four predecoded video frames with complete `VideoMetadata`; the Triton short-conv path requires CUDA.

```python
def run_transformers(model_id: str):
    import numpy as np
    import torch
    from PIL import Image
    from transformers import AutoModelForImageTextToText, AutoProcessor
    from transformers.video_utils import VideoMetadata

    model_id = str(model_id)
    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
    model = (
        AutoModelForImageTextToText.from_pretrained(
            model_id, trust_remote_code=True, dtype=torch.bfloat16, attn_implementation="sdpa"
        )
        .eval()
        .cuda()
    )
    image = Image.fromarray(np.random.randint(0, 255, (256, 256, 3), dtype=np.uint8), "RGB")
    frames = np.stack(
        [
            np.asarray(Image.new("RGB", (256, 256), color))
            for color in ((220, 30, 30), (30, 220, 30), (30, 30, 220), (220, 220, 30))
        ]
    )
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "video", "video": frames},
                {"type": "text", "text": "Describe the image and the video."},
            ],
        }
    ]
    inputs = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt",
        enable_thinking=False,
        processor_kwargs={"video_metadata": [VideoMetadata(total_num_frames=4, fps=2, frames_indices=[0, 1, 2, 3])]},
    ).to(model.device)
    assert inputs["pixel_values"].shape[0] > 0 and inputs["pixel_values_videos"].shape[0] > 0
    with torch.no_grad():
        logits = model(**inputs).logits
        assert torch.isfinite(logits).all()
        output = model.generate(**inputs, do_sample=False, max_new_tokens=32)
    print(processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=False))


if __name__ == "__main__":
    run_transformers(model_id="tiny-random/lfm3-vl")
```

### Creating this repo

- Code: [.meta/create_model.py](.meta/create_model.py)
- Model structure: [.meta/model.log](.meta/model.log)
- Environment: [.meta/env.json](.meta/env.json)

### Change log

- 2026-11-01: Initial version. Text backbone matches tiny-random/lfm3's residual/MLP widths (64/128, ten layers with the two-conv prefix and two full/conv/sliding/conv cycles) but keeps the source 4:1 Q/KV ratio with 16 Q heads and at least four KV heads (4) at head dim 32 for TP=4. The vision tower keeps the source 2:1 Q/KV ratio with 8 Q heads / 4 KV heads, which requires width 256 for its derived head dim of 32; it retains the conv/conv prefix, two full/conv cycles, patch 16, temporal patch 2, merge size 2 and axial RoPE.

*This repo is generated by GPT-6.1-Sol.*