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README.md

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+---
+base_model: lmms-lab/llava-onevision-qwen2-7b-ov
+datasets:
+- lmms-lab/EgoIT-99K
+- Ego4D
+language:
+- en
+library_name: transformers
+license: apache-2.0
+metrics:
+- accuracy
+tags:
+- multimodal
+- finetuned
+- egocentric-vision
+- video-qa
+model-index:
+- name: hpml-egoqa-baseline
+  results:
+  - task:
+      type: multimodal
+    dataset:
+      name: AI2D
+      type: ai2d
+    metrics:
+    - type: accuracy
+      value: 81.4
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: ChartQA
+      type: chartqa
+    metrics:
+    - type: accuracy
+      value: 80.0
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: DocVQA
+      type: docvqa
+    metrics:
+    - type: accuracy
+      value: 90.2
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: InfoVQA
+      type: infovqa
+    metrics:
+    - type: accuracy
+      value: 70.7
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MathVerse
+      type: mathverse
+    metrics:
+    - type: accuracy
+      value: 26.2
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MathVista
+      type: mathvista
+    metrics:
+    - type: accuracy
+      value: 63.2
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MMBench
+      type: mmbench
+    metrics:
+    - type: accuracy
+      value: 80.8
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MME-Perception
+      type: mme-perception
+    metrics:
+    - type: score
+      value: 1580
+      name: score
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MME-Cognition
+      type: mme-cognition
+    metrics:
+    - type: score
+      value: 418
+      name: score
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MMMU
+      type: mmmu
+    metrics:
+    - type: accuracy
+      value: 48.8
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MMVet
+      type: mmvet
+    metrics:
+    - type: accuracy
+      value: 57.5
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MMStar
+      type: mmstar
+    metrics:
+    - type: accuracy
+      value: 61.7
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: Seed-Bench
+      type: seed-bench
+    metrics:
+    - type: accuracy
+      value: 75.4
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: Science-QA
+      type: science-qa
+    metrics:
+    - type: accuracy
+      value: 96.0
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: ImageDC
+      type: imagedc
+    metrics:
+    - type: accuracy
+      value: 88.9
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MMLBench
+      type: mmlbench
+    metrics:
+    - type: accuracy
+      value: 77.1
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: RealWorldQA
+      type: realworldqa
+    metrics:
+    - type: accuracy
+      value: 66.3
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: Vibe-Eval
+      type: vibe-eval
+    metrics:
+    - type: accuracy
+      value: 51.7
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: LLaVA-W
+      type: llava-w
+    metrics:
+    - type: accuracy
+      value: 90.7
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: LLaVA-Wilder
+      type: l-wilder
+    metrics:
+    - type: accuracy
+      value: 67.8
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: ActNet-QA
+      type: actnet-qa
+    metrics:
+    - type: accuracy
+      value: 56.6
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: EgoSchema
+      type: egoschema
+    metrics:
+    - type: accuracy
+      value: 60.1
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MLVU
+      type: mlvu
+    metrics:
+    - type: accuracy
+      value: 64.7
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MVBench
+      type: mvbench
+    metrics:
+    - type: accuracy
+      value: 56.7
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: NextQA
+      type: nextqa
+    metrics:
+    - type: accuracy
+      value: 79.4
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: PercepTest
+      type: percepTest
+    metrics:
+    - type: accuracy
+      value: 49.7
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: SeedBench
+      type: seedbench
+    metrics:
+    - type: accuracy
+      value: 56.9
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: VideoChatGPT
+      type: videochatgpt
+    metrics:
+    - type: score
+      value: 3.49
+      name: score
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: VideoDC
+      type: videodc
+    metrics:
+    - type: score
+      value: 3.75
+      name: score
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: VideoMME
+      type: videomme
+    metrics:
+    - type: accuracy
+      value: 58.2
+      name: accuracy
+      verified: true
+---
+
+
+# HPML-EgoQA-Baseline
+
+This is a **finetuned** version of [LLaVA-OneVision-Qwen2-7B-OV](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov) for egocentric vision-language tasks.
+
+##  Table of Contents
+
+1. [Model Summary](##model-summary)
+2. [Use](##use)
+3. [Limitations](##limitations)
+4. [Training](##training)
+5. [License](##license)
+6. [Citation](##citation)
+
+## Model Summary
+
+This model is a **finetuned** version of LLaVA-OneVision-Qwen2-7B-OV, fine-tuned on [EgoIT-99K](https://huggingface.co/datasets/lmms-lab/EgoIT-99K) and Ego4D-like datasets for egocentric video question answering tasks. The base model is a 7B parameter multimodal model based on Qwen2 language model with a context window of 32K tokens, capable of understanding images, multi-image, and videos.
+
+- **Base Model:** [lmms-lab/llava-onevision-qwen2-7b-ov](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov)
+- **Finetuning Dataset:** [EgoIT-99K](https://huggingface.co/datasets/lmms-lab/EgoIT-99K) and Ego4D
+- **Languages:** English
+- **Project:** HPML (High-Performance Machine Learning) Project
+- **Team Members:** Sunidhi Tandel, Rahil, and team
+- **Institution:** HPML Project
+
+
+## Use
+
+### Intended use
+
+This model is **finetuned** on [EgoIT-99K](https://huggingface.co/datasets/lmms-lab/EgoIT-99K) and Ego4D datasets for egocentric vision-language understanding tasks, particularly video question answering from first-person perspective. The model inherits the base model's ability to interact with images, multi-image and videos, with enhanced capabilities for egocentric video understanding.
+
+**Feel free to share your generations in the Community tab!**
+
+### Generation
+
+We provide the simple generation process for using our model. For more details, you could refer to [Github](https://github.com/LLaVA-VL/LLaVA-NeXT).
+
+```python
+# pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+from llava.conversation import conv_templates, SeparatorStyle
+
+from PIL import Image
+import requests
+import copy
+import torch
+
+import sys
+import warnings
+
+warnings.filterwarnings("ignore")
+pretrained = "sunidhitandel/hpml-egoqa-baseline"  # Finetuned model
+model_name = "llava_qwen"
+device = "cuda"
+device_map = "auto"
+tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map)  # Add any other thing you want to pass in llava_model_args
+
+model.eval()
+
+url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
+image = Image.open(requests.get(url, stream=True).raw)
+image_tensor = process_images([image], image_processor, model.config)
+image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
+
+conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
+question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
+conv = copy.deepcopy(conv_templates[conv_template])
+conv.append_message(conv.roles[0], question)
+conv.append_message(conv.roles[1], None)
+prompt_question = conv.get_prompt()
+
+input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
+image_sizes = [image.size]
+
+
+cont = model.generate(
+    input_ids,
+    images=image_tensor,
+    image_sizes=image_sizes,
+    do_sample=False,
+    temperature=0,
+    max_new_tokens=4096,
+)
+text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
+print(text_outputs)
+```
+
+# Training
+
+## Base Model
+
+This model is finetuned from [LLaVA-OneVision-Qwen2-7B-OV](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov), which was trained on:
+- **Architecture:** SO400M + Qwen2
+- **Pretraining Stage:** LCS-558K, 1 epoch, projector
+- **Mid Stage:** A mixture of 4.7M high-quality synthetic data, 1 epoch, full model
+- **Final-Image Stage:** A mixture of 3.6M single-image data, 1 epoch, full model
+- **OneVision Stage:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
+
+## Finetuning
+
+- **Base Model:** lmms-lab/llava-onevision-qwen2-7b-ov
+- **Finetuning Dataset:** EgoIT-99K and Ego4D (egocentric video QA data)
+- **Task:** Egocentric video question answering
+- **Precision:** bfloat16
+- **Method:** Full fine-tuning / LoRA (depending on configuration)
+
+## Hardware & Software
+
+- **GPUs:** Nvidia A100 (for finetuning)
+- **Orchestration:** [Huggingface Trainer](https://huggingface.co/docs/transformers/main_classes/trainer)
+- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
+
+# Citation
+
+If you use this finetuned model, please cite both the base model and this work:
+
+```bibtex
+@article{li2024llavaonevision,
+      title={LLaVA-OneVision},
+      author={Li, Bo and others},
+      journal={arXiv preprint arXiv:2408.03326},
+      year={2024}
+}
+
+@misc{hpml-egoqa-baseline,
+      title={HPML-EgoQA-Baseline: Finetuned LLaVA-OneVision for Egocentric Video QA},
+      author={Tandel, Sunidhi and Rahil and HPML Project Team},
+      year={2024},
+      howpublished={\url{https://huggingface.co/sunidhitandel/hpml-egoqa-baseline}},
+      note={HPML Project - High-Performance Machine Learning for Egocentric Vision}
+}
+```
+
+## Acknowledgments
+
+This work is part of the HPML (High-Performance Machine Learning) Project. We thank the LLaVA-OneVision team for providing the base model and the EgoIT-99K dataset contributors.
+
+**Team Members:**
+- Sunidhi Tandel
+- Rahil
+- HPML Project Team

added_tokens.json

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+  "<|im_end|>": 151645,
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+}

config.json

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+  "architectures": [
+    "LlavaQwenForCausalLM"
+  ],
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+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "image_token_index": 151646,
+  "image_aspect_ratio": "anyres_max_9",
+  "image_crop_resolution": null,
+  "image_grid_pinpoints": [
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+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "mm_hidden_size": 1152,
+  "mm_patch_merge_type": "spatial_unpad",
+  "mm_projector_lr": null,
+  "mm_projector_type": "mlp2x_gelu",
+  "mm_resampler_type": null,
+  "mm_spatial_pool_mode": "bilinear",
+  "mm_tunable_parts": "mm_vision_tower,mm_mlp_adapter,mm_language_model",
+  "mm_use_im_patch_token": false,
+  "mm_use_im_start_end": false,
+  "mm_vision_select_feature": "patch",
+  "mm_vision_select_layer": -2,
+  "mm_vision_tower": "google/siglip-so400m-patch14-384",
+  "mm_vision_tower_lr": 2e-06,
+  "model_type": "llava",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pos_skipping_range": 4096,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "tokenizer_model_max_length": 32768,
+  "tokenizer_padding_side": "right",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.0.dev0",
+  "use_cache": true,
+  "use_mm_proj": true,
+  "use_pos_skipping": false,
+  "use_sliding_window": false,
+  "vision_tower_pretrained": null,
+  "vocab_size": 152064
+}

generation_config.json

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+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.40.0.dev0"
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preprocessor_config.json

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+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
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+}

special_tokens_map.json

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+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+}

tokenizer_config.json

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+  "added_tokens_decoder": {
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+      "normalized": false,
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+      "special": true
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": false,
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+      "single_word": false,
+      "special": true
+    }
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+  "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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+  "eos_token": "<|im_end|>",
+  "errors": "replace",
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+  "padding_side": "right",
+  "processor_class": "LlavaProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
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