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