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--- |
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license: gemma |
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datasets: |
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- BUAADreamer/llava-en-zh-300k |
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language: |
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- en |
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- zh |
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library_name: transformers |
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pipeline_tag: image-text-to-text |
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base_model: google/paligemma-3b-mix-448 |
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inference: false |
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tags: |
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- paligemma |
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- llama-factory |
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- mllm |
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- vlm |
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--- |
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# PaliGemma-3B-Chat-v0.2 |
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This model is fine-tuned from [google/paligemma-3b-mix-448](https://huggingface.co/google/paligemma-3b-mix-448) for multiturn chat completions. |
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Try our live demo at: https://huggingface.co/spaces/llamafactory/PaliGemma-3B-Chat-v0.2 |
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## Usage |
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```python |
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import requests |
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import torch |
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from PIL import Image |
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from transformers import AutoModelForVision2Seq, AutoProcessor, AutoTokenizer, TextStreamer |
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model_id = "BUAADreamer/PaliGemma-3B-Chat-v0.2" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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processor = AutoProcessor.from_pretrained(model_id) |
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model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype="auto", device_map="auto") |
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) |
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" |
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image = Image.open(requests.get(url, stream=True).raw) |
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pixel_values = processor(images=[image], return_tensors="pt").to(model.device)["pixel_values"] |
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messages = [ |
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{"role": "user", "content": "What is in this image?"} |
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] |
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input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") |
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image_token_id = tokenizer.convert_tokens_to_ids("<image>") |
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image_prefix = torch.empty((1, getattr(processor, "image_seq_length")), dtype=input_ids.dtype).fill_(image_token_id) |
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input_ids = torch.cat((image_prefix, input_ids), dim=-1).to(model.device) |
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generate_ids = model.generate(input_ids, pixel_values=pixel_values, streamer=streamer, max_new_tokens=50) |
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``` |
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## Training procedure |
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We used [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) to fine-tune this model. During fine-tuning, we freezed the vision tower and adjusted the parameters in the language model and projector layer. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.000003 |
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- num_train_epochs: 2.0 |
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- train_batch_size: 4 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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- seed: 42 |
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- lr_scheduler_type: cosine |
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- mixed_precision_training: bf16 |
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<details> |
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<summary><b>Show Llama Factory Config [CLICK TO EXPAND]</b></summary> |
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```yaml |
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### model |
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model_name_or_path: google/paligemma-3b-mix-448 |
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visual_inputs: true |
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### method |
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stage: sft |
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do_train: true |
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finetuning_type: full |
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### ddp |
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ddp_timeout: 180000000 |
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deepspeed: examples/deepspeed/ds_z3_config.json |
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### dataset |
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dataset: identity,llava_150k_en,llava_150k_zh |
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template: gemma |
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cutoff_len: 1536 |
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overwrite_cache: true |
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preprocessing_num_workers: 16 |
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tokenized_path: cache/paligemma-identity-llava-zh-en-300k |
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### output |
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output_dir: models/paligemma-3b-chat-v0.2 |
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logging_steps: 10 |
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save_steps: 1000 |
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plot_loss: true |
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### train |
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per_device_train_batch_size: 1 |
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gradient_accumulation_steps: 16 |
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learning_rate: 0.000003 |
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num_train_epochs: 2.0 |
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lr_scheduler_type: cosine |
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warmup_steps: 50 |
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bf16: true |
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do_eval: false |
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``` |
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</details> |
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### Framework versions |
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- Pytorch 2.3.0 |
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- Transformers 4.41.0 |
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## Evaluation Results |
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| Model | MMMU_Val | CMMMU_Val | |
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| :--------------------: | :------: | :-------: | |
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| Yi-VL-6B | 36.8 | 32.2 | |
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| Paligemma-3B-Chat-v0.2 | 33.0 | 29.0 | |
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