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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: image-text-to-text
+tags:
+- multimodal
+- mllm
+- knowledge-distillation
+- mobilevlm
+- mobilellama
+base_model: mtgv/MobileLLaMA-1.4B-Chat
+---
+
+# Cosine-Beta-KD-Task
+
+A 1.7B multimodal LLM checkpoint distilled with **Cosine-KD + Beta-KD (Task-level uncertainty weighting)**,
+built on top of MobileVLM with
+[`MobileLLaMA-1.4B-Chat`](https://huggingface.co/mtgv/MobileLLaMA-1.4B-Chat) as
+the language backbone.
+
+This checkpoint corresponds to the **`Beta-KD (Task)`** row of the model
+zoo in [Beta-KD: Uncertainty-Aware Knowledge Distillation for Multimodal Large
+Language Models](https://arxiv.org/abs/2603.21426).
+
+## Model Details
+
+| Item | Value |
+|------|-------|
+| Architecture | MobileVLM (CLIP visual encoder + LDP projector + MobileLLaMA LLM) |
+| Language model | MobileLLaMA 1.4B |
+| Distillation losses | Cosine-KD (logit alignment) + Beta-KD task-level uncertainty loss |
+| Training step | `checkpoint-18000` |
+| Total params | ~1.7B |
+| Precision | fp16 |
+
+## Evaluation
+
+Evaluated on six standard multimodal benchmarks (no beam search, greedy
+decoding to match the chat-demo behavior).
+
+| Method | LLM | MME<sup>P</sup> | MME<sup>A</sup> | GQA | VQA<sup>T</sup> | POPE | MMB<sup>dev</sup> | SQA<sup>I</sup> | Avg. |
+|--------|-----|------|------|------|------|------|------|------|------|
+| Cosine-KD baseline | MobileLLaMA 1.4B | 1308.4 | 65.4 | 59.9 | 52.2 | 84.6 | 57.1 | 61.3 | 63.4 |
+| **+ Beta-KD (Task)** *(this model)* | MobileLLaMA 1.4B | **1352.0** | **67.6** | 60.8 | 53.9 | 85.4 | 59.1 | 61.2 | 64.7 |
+| + Beta-KD (Instance) | MobileLLaMA 1.4B | 1350.3 | 67.5 | **61.2** | **54.2** | **86.0** | **60.2** | **62.9** | **65.3** |
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+repo_id = "jsun39/Cosine-Beta-KD-Task"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    repo_id,
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+).cuda()
+```
+
+For full inference (image + text), please follow the inference example in the
+[Beta-KD repo](https://github.com/Jingchensun/beta-kd) — the visual encoder /
+projector loading, image preprocessing, and chat template are described there.
+
+## Files
+
+This repo contains only the files needed for inference:
+
+- `pytorch_model.bin` — fp16 weights
+- `config.json`, `generation_config.json`
+- `tokenizer.model`, `tokenizer_config.json`, `special_tokens_map.json`
+
+DeepSpeed optimizer / RNG / trainer states are intentionally **not** uploaded.
+
+## Citation
+
+```bibtex
+@article{sun2026betakd,
+  title   = {Beta-KD: Uncertainty-Aware Knowledge Distillation for Multimodal
+             Large Language Models},
+  author  = {Sun, Jingchen and others},
+  journal = {arXiv preprint arXiv:2603.21426},
+  year    = {2026}
+}
+```
+
+## License
+
+Released under the Apache-2.0 license, inheriting from MobileVLM and
+MobileLLaMA. The visual encoder and any third-party data follow their original
+licenses.