Table of Links Abstract and 1 Introduction 2. Related Work 3. LLaVA-Phi and 3.1. Training 3.2. Qualitative Results 4. Experiments 5. Conclusion, Limitation, and Future Works and References 3. LLaVA-Phi Our overall network architecture is similar to LLaVA-1.5. We use the pre-trained CLIP ViT-L/14 with a resolution of 336x336 as the visual encoder. A two-layer MLP is adopted to improve the connection of the visual encoder and LLM. 3.1. Training Supervised fine-tuning on Phi-2. The publicly released Phi-2 model has not undergone fine-tuning. Previous research indicates that even a small amount of high-quality data can significantly enhance performance in areas such as mathematics, language reasoning, and coding tasks. In light of this, we employed supervised fine-tuning to further train Phi-2 using a select set of premium data. This data was organized in the Vicuna format. For our Supervised Fine-Tuning (SFT) data, we utilized ShareGPT from an open-source platform. The training was conducted over two epochs, beginning with an initial learning rate of 3e-5, which was linearly decreased over time. Our findings suggest that while this step might be optional, applying SFT to Phi-2 does result in modest improvements across most benchmarks. Training LLaVA-Phi. Our training approach follows the pipeline used for LLaVA1.5, consisting of a pretraining stage and a subsequent instruction tuning phase. Initially, we kept the vision encoder and Phi-2 static, focusing exclusively on training the efficient projector. This step is followed by a comprehensive fine-tuning of both the projector and the language model (LLM), aiming to enhance their capabilities in visual comprehension and language processing. For pre-training, we utilize a filtered subset of the CC-595K dataset [24] over one epoch, applying an initial learning rate of 1e-3 and a batch size of 256. Then, we finetune the model on LLaVA-Instruct-150K dataset for 1 epoch at a learning rate of 2e-5 and a batch size of 256. We implement a weight decay of 0.1 and utilize the Adam optimizer, characterized by momentum parameters of 0.9 and 0.98, and an epsilon value of 1e-7. We fine-tune all parameters in LLM instead of using LoRA. Computational Cost. Similar to LLaVA1.5, our training process is structured in two stages. For LLaVA-Phi, the pretraining phase takes 1.5 hours, followed by 8 hours dedicated to visual instruction tuning, utilizing 8 A100 GPUs. The integration of techniques such as LoRA [15] and QLoRA [9] has the potential to significantly reduce training time, a possibility we plan to explore in future work. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Yichen Zhu, Midea Group;
(2) Minjie Zhu, Midea Group and East China Normal University;
(3) Ning Liu, Midea Group;
(4) Zhicai Ou, Midea Group;
(5) Xiaofeng Mou, Midea Group. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. LLaVA-Phi and 3.1. Training 3. LLaVA-Phi and 3.1. Training 3.2. Qualitative Results 3.2. Qualitative Results 4. Experiments 4. Experiments 5. Conclusion, Limitation, and Future Works and References 5. Conclusion, Limitation, and Future Works and References 3. LLaVA-Phi Our overall network architecture is similar to LLaVA-1.5. We use the pre-trained CLIP ViT-L/14 with a resolution of 336x336 as the visual encoder. A two-layer MLP is adopted to improve the connection of the visual encoder and LLM. 3.1. Training Supervised fine-tuning on Phi-2. The publicly released Phi-2 model has not undergone fine-tuning. Previous research indicates that even a small amount of high-quality data can significantly enhance performance in areas such as mathematics, language reasoning, and coding tasks. In light of this, we employed supervised fine-tuning to further train Phi-2 using a select set of premium data. This data was organized in the Vicuna format. For our Supervised Fine-Tuning (SFT) data, we utilized ShareGPT from an open-source platform. The training was conducted over two epochs, beginning with an initial learning rate of 3e-5, which was linearly decreased over time. Our findings suggest that while this step might be optional, applying SFT to Phi-2 does result in modest improvements across most benchmarks. Supervised fine-tuning on Phi-2. Training LLaVA-Phi. Our training approach follows the pipeline used for LLaVA1.5, consisting of a pretraining stage and a subsequent instruction tuning phase. Initially, we kept the vision encoder and Phi-2 static, focusing exclusively on training the efficient projector. This step is followed by a comprehensive fine-tuning of both the projector and the language model (LLM), aiming to enhance their capabilities in visual comprehension and language processing. Training LLaVA-Phi. For pre-training, we utilize a filtered subset of the CC-595K dataset [24] over one epoch, applying an initial learning rate of 1e-3 and a batch size of 256. Then, we finetune the model on LLaVA-Instruct-150K dataset for 1 epoch at a learning rate of 2e-5 and a batch size of 256. We implement a weight decay of 0.1 and utilize the Adam optimizer, characterized by momentum parameters of 0.9 and 0.98, and an epsilon value of 1e-7. We fine-tune all parameters in LLM instead of using LoRA. Computational Cost. Similar to LLaVA1.5, our training process is structured in two stages. For LLaVA-Phi, the pretraining phase takes 1.5 hours, followed by 8 hours dedicated to visual instruction tuning, utilizing 8 A100 GPUs. The integration of techniques such as LoRA [15] and QLoRA [9] has the potential to significantly reduce training time, a possibility we plan to explore in future work. Computational Cost. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Yichen Zhu, Midea Group; (2) Minjie Zhu, Midea Group and East China Normal University; (3) Ning Liu, Midea Group; (4) Zhicai Ou, Midea Group; (5) Xiaofeng Mou, Midea Group. Authors: Authors: (1) Yichen Zhu, Midea Group; (2) Minjie Zhu, Midea Group and East China Normal University; (3) Ning Liu, Midea Group; (4) Zhicai Ou, Midea Group; (5) Xiaofeng Mou, Midea Group.

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