deepvk
/

llava-gemma-2b-lora

@@ -1,199 +1,109 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- deepvk/LLaVA-Instruct-ru
+- Lin-Chen/ShareGPT4V
+- deepvk/GQA-ru
+language:
+- ru
+- en
+base_model: google/gemma-2b-it
+pipeline_tag: image-text-to-text
 ---
+# LLaVA-Gemma-2b-LORA
+LLaVA-Gemma-2b-LORA is a Vision-Language Model (VLM) based on [`google/gemma-2b-it`](https://huggingface.co/google/gemma-2b-it) model
+and trained in original LLaVA setup using LORA. This model is primarily adapted to work with Russian, but still capable to work with English.
+## Usage
+Model usage is simple via `transformers` API
+```python
+import requests
+from PIL import Image
+from transformers import AutoProcessor, AutoTokenizer, LlavaForConditionalGeneration
+model_name = "deepvk/llava-gemma-2b-lora"
+model = LlavaForConditionalGeneration.from_pretrained(model_name)
+processor = AutoProcessor.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+img = Image.open(requests.get(url, stream=True).raw)
+messages = [
+    {"role": "user", "content": "<image>\nОпиши картинку несколькими словами."}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = processor(images=[img], text=text, return_tensors="pt")
+generate_ids = model.generate(**inputs, max_new_tokens=30)
+answer = tokenizer.decode(generate_ids[0, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(answer)
+```
+Use the `<image>` tag to point to an image in the text and follow the chat template for a multi-turn conversation.
+The model is capable of chatting without any images or working with multiple images in a conversation, but this behavior has not been tested.
+The model format allows it to be directly used in popular frameworks,
+e.g. you can test the model using [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval), see Results section for details.
+## Train
+To train this model, we follow the original LLaVA pipeline and reuse [`haotian-liu/LLaVA`](https://github.com/haotian-liu/LLaVA) framework.
+The model was trained in two stages:
+1. The adapter was trained using pre-training data from [`ShareGPT4V`](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V).
+2. Instruction tuning included training the LLM and the adapter, for this we use:
+    * [`deepvk/LLaVA-Instruct-ru`](https://huggingface.co/datasets/deepvk/LLaVA-Instruct-ru) — our new dataset of VLM instructions in Russian
+    * [`deepvk/GQA-ru`](https://huggingface.co/datasets/deepvk/GQA-ru) — the training part of the popular GQA test, translated into Russian, we used the post-prompt "Ответь одним словом. ".
+    * We also used instruction data from ShareGPT4V.
+The entire training process took 3 days on a single A100 40GB.
+## Results
+The model's performance was evaluated using [`lmms-eval`](https://github.com/EvolvingLMMs-Lab/lmms-eval/tree/main) framework
+```bash
+accelerate launch -m lmms_eval --model llava_hf --model_args pretrained="deepvk/llava-gemma-2b-lora" \
+  --tasks gqa-ru,mmbench_ru_dev,gqa,mmbench_en_dev --batch_size 1 \
+  --log_samples --log_samples_suffix llava-saiga-8b --output_path ./logs/
+```
+| Model                                                                                           | GQA          | GQA-ru       | MMBench      | MMBench-ru   |
+| ----------------------------------------------------------------------------------------------- |:------------:|:------------:|:------------:|:------------:|
+| `deepvk/llava-gemma-2b-lora` [this model]                                                       | 56.39        | <u>46.37</u> | <u>51.72</u> | <u>40.19</u> |
+| [`Intel/llava-gemma-2b`](https://huggingface.co/Intel/llava-gemma-2b)                           | <u>59.80</u> | 0.20         | 39.40        | 28.30        |
+| [`deepvk/llava-saiga-8b`](https://huggingface.co/deepvk/llava-saiga-8b)                         | 62.00        | **51.44**    | 64.26        | **56.65**    |
+| [`llava-hf/llava-1.5-7b-hf`](https://huggingface.co/llava-hf/llava-1.5-7b-hf)                   | 61.31        | 28.39        | 62.97        | 52.25        |
+| [`llava-hf/llava-v1.6-mistral-7b-hf`](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) | **64.65**    | 6.65         | **67.70**    | 48.80        |
+*Note*: for MMBench we didn't use OpenAI API for finding quantifier in generated string. Therefore, the score is similar to Exact Match as in GQA benchmark.
+## Citation
+```
+@misc{liu2023llava,
+  title={Visual Instruction Tuning},
+  author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
+  publisher={NeurIPS},
+  year={2023},
+}
+```
+```
+@misc{deepvk2024llava-gemma-2b-lora,
+	title={LLaVA-Gemma-2b-LORA},
+	author={Belopolskih, Daniil and Spirin, Egor},
+	url={https://huggingface.co/deepvk/llava-gemma-2b-lora},
+	publisher={Hugging Face}
+	year={2024},
+}
+```