Paligemma 3B Chat V0.2

🚀 PaliGemma-3B-Chat-v0.2

This model is fine-tuned from google/paligemma-3b-mix-448 for multiturn chat completions. It offers an effective solution for image - text - to - text tasks, enabling seamless interaction between images and text in chat scenarios.

Try our live demo at: https://huggingface.co/spaces/llamafactory/PaliGemma-3B-Chat-v0.2

🚀 Quick Start

The following sections will guide you through the usage, training, and evaluation of the PaliGemma-3B-Chat-v0.2 model.

✨ Features

• Multiturn Chat: Fine - tuned for multiturn chat completions, enhancing the interaction experience.
• Image - Text Integration: Capable of handling image - text - to - text tasks, leveraging both visual and textual information.

💻 Usage Examples

Basic Usage

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)

🔧 Technical Details

Training procedure

We used 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

### 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

Framework versions

• Pytorch 2.3.0
• Transformers 4.41.0

📚 Documentation

Evaluation Results

📄 License

The license of this project is gemma.