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README.md

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+---
+license: apache-2.0
+library_name: vla-foundry
+tags:
+  - foundry
+  - vla_foundry
+  - vlm
+  - image-text-to-text
+---
+
+# Foundry-VLM-1.3B-200M
+
+A 1.3B parameter vision-language model trained on 200M image-caption samples, part of the [VLA Foundry](https://github.com/TRI-ML/vla_foundry) collection.
+
+## Model Description
+
+- **Architecture:** ViT encoder (12 layers, 768 hidden dim, patch size 14, pixel-shuffle 2x) + Transformer decoder (24 layers, 2048 hidden dim, 16 heads)
+- **Parameters:** 1.3B (non-embedding)
+- **Processor:** SmolVLM2
+- **Training data:** 200M image-caption pairs from DataComp-DR-1B
+- **LR schedule:** Warmup + constant for 165M samples, then 35M samples of cosine decay
+- **LLM backbone:** Initialized from [Foundry-LLM-1.2B-800B](https://huggingface.co/TRI-ML/Foundry-LLM-1.2B-800B)
+
+Continuation of [Foundry-VLM-1.3B-165M](https://huggingface.co/TRI-ML/Foundry-VLM-1.3B-165M) with an additional 35M samples of cosine-decayed training. Used as the vision-language backbone for the Foundry-VLA-1.7B action models.
+
+## Evaluation Results
+
+COCO-val captioning:
+
+| BLEU-1 | BLEU-2 | BLEU-3 | BLEU-4 | ROUGE-L | CIDEr |
+|---|---|---|---|---|---|
+| 58.64 | 38.62 | 24.49 | 15.57 | 38.17 | 55.14 |
+
+## Usage
+
+```bash
+git clone https://github.com/TRI-ML/vla_foundry.git
+cd vla_foundry
+pip install -e .
+```
+
+```python
+from vla_foundry.models.base_model import BaseModel
+model = BaseModel.from_pretrained("TRI-ML/Foundry-VLM-1.3B-200M")
+```
+
+## Links
+
+- **Project page:** [tri-ml.github.io/vla_foundry](https://tri-ml.github.io/vla_foundry/)
+- **Paper:** [VLA Foundry (arXiv 2604.19728)](https://arxiv.org/abs/2604.19728)
+- **Code:** [github.com/TRI-ML/vla_foundry](https://github.com/TRI-ML/vla_foundry)
+- **Collection:** [VLA Foundry collection](https://huggingface.co/collections/TRI-ML/vla-foundry)