BUAADreamer
/

PaliGemma-3B-Chat-v0.2

@@ -1,200 +1,103 @@
----
-library_name: transformers
-tags:
-- llama-factory
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

+# 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 -->
+<!-- ![example_en](assets/example_en.png)
+![example_zh](assets/example_zh.png)
+![example_ja](assets/example_ja.png) -->
+## 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