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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- meta-llama/Llama-3.2-11B-Vision-Instruct |
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pipeline_tag: visual-question-answering |
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library_name: transformers |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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Llama-3.2V-11B-cot is the first version of [LLaVA-o1](https://github.com/PKU-YuanGroup/LLaVA-o1), which is a visual language model capable of spontaneous, systematic reasoning. |
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## Model Details |
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<!-- Provide a longer summary of what this model is. --> |
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- **License:** apache-2.0 |
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- **Finetuned from model:** meta-llama/Llama-3.2-11B-Vision-Instruct |
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## Benchmark Results |
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| MMStar | MMBench | MMVet | MathVista | AI2D | Hallusion | Average | |
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|--------|---------|-------|-----------|------|-----------|---------| |
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| 57.6 | 75.0 | 60.3 | 54.8 | 85.7 | 47.8 | 63.5 | |
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## Reproduction |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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To reproduce our results, you should use [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) and the following settings. |
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| Parameter | Value | |
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|-------------------|---------| |
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| do_sample | True | |
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| temperature | 0.6 | |
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| top_p | 0.9 | |
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| max_new_tokens | 2048 | |
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You may change them in [this file](https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/vlm/llama_vision.py), line 80-83, and modify the max_new_tokens throughout the file. |
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Note: We follow the same settings as Llama-3.2-11B-Vision-Instruct, except that we extend the max_new_tokens to 2048. |
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After you get the results, you should filter the model output and only **keep the outputs between \<CONCLUSION\> and \</CONCLUSION\>**. |
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This shouldn't have any difference in theory, but empirically we observe some performance difference because the jugder GPT-4o can be inaccurate sometimes. |
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By keeping the outputs between \<CONCLUSION\> and \</CONCLUSION\>, most answers can be direclty extracted using VLMEvalKit system, which can be much less biased. |
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## How to Get Started with the Model |
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You can use the inference code for Llama-3.2-11B-Vision-Instruct. |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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The model is trained on the LLaVA-o1-100k dataset (to be released). |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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The model is finetuned on [llama-recipes](https://github.com/Meta-Llama/llama-recipes) with the following settings. |
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Using the same setting should accurately reproduce our results. |
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| Parameter | Value | |
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|-------------------------------|---------------------------------------------------| |
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| FSDP | enabled | |
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| lr | 1e-5 | |
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| num_epochs | 3 | |
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| batch_size_training | 4 | |
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| use_fast_kernels | True | |
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| run_validation | False | |
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| batching_strategy | padding | |
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| context_length | 4096 | |
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| gradient_accumulation_steps | 1 | |
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| gradient_clipping | False | |
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| gradient_clipping_threshold | 1.0 | |
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| weight_decay | 0.0 | |
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| gamma | 0.85 | |
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| seed | 42 | |
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| use_fp16 | False | |
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| mixed_precision | True | |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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The model may generate biased or offensive content, similar to other VLMs, due to limitations in the training data. |
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Technically, the model's performance in aspects like instruction following still falls short of leading industry models. |
