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BAAI-RoboBrain2.0-3B/README.md

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+---
+library_name: transformers
+license: other
+tags:
+- llama-factory
+- full
+- generated_from_trainer
+model-index:
+- name: output_1
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# output_1
+
+This model is a fine-tuned version of [/data2/wuxinrui/RoboBrain2.0/HF_Models/BAAI-RoboBrain2.0-7B_budgetthinker_3ratio](https://huggingface.co//data2/wuxinrui/RoboBrain2.0/HF_Models/BAAI-RoboBrain2.0-7B_budgetthinker_3ratio) on the region_ref_max_points_10_absxy_int_2k_cot_processed_shorten_shorten2_budgetthinker_3ratio_mllm, the region_ref_max_points_10_absxy_int_2k_cot_processed_shorten_budgetthinker_3ratio_mllm, the region_ref_max_points_10_absxy_int_2k_cot_processed_budgetthinker_3ratio_mllm, the sharerobot_trajectory_2k_cot_processed_shorten_shorten2_budgetthinker_3ratio_mllm, the sharerobot_trajectory_2k_cot_processed_shorten_budgetthinker_3ratio_mllm, the sharerobot_trajectory_2k_cot_processed_budgetthinker_3ratio_mllm, the AGD_affordance_train_common_1336_shorten_shorten2_budgetthinker_3ratio_mllm, the AGD_affordance_train_common_1336_shorten_budgetthinker_3ratio_mllm, the AGD_affordance_train_common_1336_budgetthinker_3ratio_mllm, the osd_choice_qa_absolute_position_2k_cot_processed_shorten_shorten2_budgetthinker_3ratio_mllm, the osd_choice_qa_absolute_position_2k_cot_processed_shorten_budgetthinker_3ratio_mllm, the osd_choice_qa_absolute_position_2k_cot_processed_budgetthinker_3ratio_mllm, the osd_reasoning_qa_2k_cot_processed_shorten_shorten2_budgetthinker_3ratio_mllm, the osd_reasoning_qa_2k_cot_processed_shorten_budgetthinker_3ratio_mllm and the osd_reasoning_qa_2k_cot_processed_budgetthinker_3ratio_mllm datasets.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-06
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 6
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 12
+- total_eval_batch_size: 48
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 2.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.51.3
+- Pytorch 2.8.0+cu128
+- Datasets 3.6.0
+- Tokenizers 0.21.1

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+  },
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+    "\n<remaining>1/3</remaining>\n",
+    "\n<remaining>2/3</remaining>\n"
+  ],
+  "bos_token": null,
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "processor_class": "Qwen2_5_VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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+}

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BAAI-RoboBrain2.0-7B/README.md

@@ -0,0 +1,167 @@
+---
+extra_gated_prompt: "You agree to not use the model to conduct experiments that cause harm to human subjects."
+extra_gated_fields:
+  Company/Organization: text
+  Country: country
+
+license: apache-2.0
+library_name: transformers
+pipeline_tag: robotics
+---
+
+<div align="center">
+<img src="https://github.com/FlagOpen/RoboBrain2.0/raw/main/assets/logo2.png" width="500"/>
+</div>
+
+# RoboBrain 2.0: See Better. Think Harder. Do Smarter. 
+
+
+<p align="center">
+        </a>&nbsp&nbsp⭐️ <a href="https://superrobobrain.github.io/">Project</a></a>&nbsp&nbsp | &nbsp&nbsp⭐️ <a href="https://github.com/FlagOpen/RoboBrain2.0">Github</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://www.modelscope.cn/models/BAAI/RoboBrain2.0-7B">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp📑 <a href="https://huggingface.co/papers/2507.02029">Technical Report</a>&nbsp&nbsp | &nbsp&nbsp💬 <a href="https://github.com/FlagOpen/RoboBrain2.0/raw/main/assets/wechat.png">WeChat</a>
+</p>
+<p align="center">
+        </a>&nbsp&nbsp🎯 <a href="https://github.com/FlagOpen/RoboOS">RoboOS</a>: An Efficient Open-Source Multi-Robot Coordination System for RoboBrain.
+</p>
+<p align="center">
+</a>&nbsp&nbsp🌍 <a href="https://github.com/FlagOpen/RoboBrain">RoboBrain 1.0</a>: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete.
+</p>
+
+## 🗞️ News
+- **`2025-07-23`**: 🤗 **[RoboBrain 2.0-3B](https://huggingface.co/BAAI/RoboBrain2.0-3B) model checkpoint has been released in Huggingface.**
+- **`2025-07-03`**: 🤗 [RoboBrain 2.0-32B](https://huggingface.co/BAAI/RoboBrain2.0-32B) model checkpoint has been released in Huggingface.
+- **`2025-06-07`**: 🎉 We highlight the training framework ([FlagScale](https://github.com/FlagOpen/FlagScale)) developed by *BAAI Framework R&D team*, and the evaluation framework ([FlagEvalMM](https://github.com/flageval-baai/FlagEvalMM)) by *BAAI FlagEval team*. Both are used for RoboBrain 2.0. 
+- **`2025-06-06`**: 🤗 [RoboBrain 2.0-7B](https://huggingface.co/BAAI/RoboBrain2.0-7B) model checkpoint has been released in Huggingface..
+- **`2025-06-06`**: 🔥 We're excited to announce the release of our more powerful [RoboBrain 2.0](https://superrobobrain.github.io/).
+- **`2025-04-11`**: 🎉 [RoboBrain 1.0](https://github.com/FlagOpen/RoboBrain/) was selected for CVPR 2025's official [Embodied AI Trends Commentary](https://cvpr.thecvf.com/Conferences/2025/News/AI_Enhanced_Robotics).
+- **`2025-02-27`**: 🌍 [RoboBrain 1.0](http://arxiv.org/abs/2502.21257/) was accepted to CVPR2025.
+  
+## 🔥 Overview
+We are thrilled to announce the launch of **RoboBrain 2.0**, the most powerful open-source embodied brain model to date. Compared to its predecessor, RoboBrain 1.0, the new version is designed to unify perception, reasoning, and planning for complex embodied tasks in physical environments. RoboBrain 2.0 comes in three variants: an ultra-lightweight 3B model, a lightweight 7B model, and a full-scale 32B model, all featuring a heterogeneous architecture with a vision encoder and a language model.
+Despite its compact size, RoboBrain 2.0 delivers exceptional performance across a wide range of embodied reasoning tasks. The newly released 3B model is optimized for resource-constrained scenarios, being extremely lightweight and easy to deploy, with spatial understanding capabilities comparable to the 7B model, making it ideal for edge devices and real-time applications. The 7B model strikes a balance between performance and computational requirements, suitable for diverse scenarios, while the 32B model achieves leading results in most spatial and temporal benchmarks, surpassing prior open-source and proprietary models. RoboBrain 2.0 supports critical real-world embodied intelligence capabilities, including spatial understanding (e.g., affordance prediction, spatial referring, trajectory forecasting) and temporal decision-making (e.g., closed-loop interaction, multi-agent long-horizon planning, and real-time scene memory).
+This report details the model architecture, data construction, multi-stage training strategies, infrastructure, and practical applications. We hope RoboBrain 2.0 advances embodied AI research and serves as a practical step toward building generalist embodied agents.
+<div align="center">
+<img src="https://github.com/FlagOpen/RoboBrain2.0/raw/main/assets/results.png" />
+</div>
+
+<!-- ## 🗞️ News
+- **`2025-07-23`**: 🤗 **[RoboBrain 2.0-3B](https://huggingface.co/BAAI/RoboBrain2.0-3B) model checkpoint has been released in Huggingface.**
+- **`2025-07-03`**: 🤗 [RoboBrain 2.0-32B](https://huggingface.co/BAAI/RoboBrain2.0-32B) model checkpoint has been released in Huggingface.
+- **`2025-06-07`**: 🎉 We highlight the training framework ([FlagScale](https://github.com/FlagOpen/FlagScale)) developed by *BAAI Framework R&D team*, and the evaluation framework ([FlagEvalMM](https://github.com/flageval-baai/FlagEvalMM)) by *BAAI FlagEval team*. Both are used for RoboBrain 2.0. 
+- **`2025-06-06`**: 🤗 [RoboBrain 2.0-7B](https://huggingface.co/BAAI/RoboBrain2.0-7B) model checkpoint has been released in Huggingface..
+- **`2025-06-06`**: 🔥 We're excited to announce the release of our more powerful [RoboBrain 2.0](https://superrobobrain.github.io/).
+- **`2025-04-11`**: 🎉 [RoboBrain 1.0](https://github.com/FlagOpen/RoboBrain/) was selected for CVPR 2025's official [Embodied AI Trends Commentary](https://cvpr.thecvf.com/Conferences/2025/News/AI_Enhanced_Robotics).
+- **`2025-02-27`**: 🌍 [RoboBrain 1.0](http://arxiv.org/abs/2502.21257/) was accepted to CVPR2025. -->
+
+
+## 📆 Todo
+- [x] Release model checkpoint for RoboBrain 2.0-3B
+- [x] Release model checkpoint for RoboBrain 2.0-7B
+- [x] Release quick inference example for RoboBrain 2.0
+- [x] Release training codes for RoboBrain 2.0
+- [x] Release model checkpoint for RoboBrain 2.0-32B
+
+## 🚀 Features
+**RoboBrain 2.0** supports ***interactive reasoning*** with long-horizon planning and closed-loop feedback, ***spatial perception*** for precise point and bbox prediction from complex instructions, ***temporal perception*** for future trajectory estimation, and ***scene reasoning*** through real-time structured memory construction and update.
+
+<div align="center">
+<img src="https://github.com/FlagOpen/RoboBrain2.0/raw/main/assets/visualization.png" />
+</div>
+
+
+## ⭐️ Architecture
+**RoboBrain 2.0** supports ***multi-image***, ***long video***, and ***high-resolution visual inputs***, along with complex task instructions and structured ***scene graphs*** on the language side. Visual inputs are processed via a Vision Encoder and MLP Projector, while textual inputs are tokenized into a unified token stream. All inputs are fed into a ***LLM Decoder*** that performs ***long-chain-of-thought reasoning*** and outputs structured plans, spatial relations, and both ***relative*** and ***absolute coordinates***.
+
+<div align="center">
+<img src="https://github.com/FlagOpen/RoboBrain2.0/raw/main/assets/arch.png" />
+</div>
+
+
+## 🤗 Model Zoo
+
+
+| Models               | Checkpoint                                                     | Description                                                | 
+|----------------------|----------------------------------------------------------------|------------------------------------------------------------|
+| RoboBrain 2.0 3B      | [🤗 BAAI/RoboBrain2.0-3B](https://huggingface.co/BAAI/RoboBrain2.0-3B)   | 3B parameter version of the RoboBrain2.0                   | 
+| RoboBrain 2.0 7B      | [🤗 BAAI/RoboBrain2.0-7B](https://huggingface.co/BAAI/RoboBrain2.0-7B)   | 7B parameter version of the RoboBrain2.0                   | 
+| RoboBrain 2.0 32B      | [🤗 BAAI/RoboBrain2.0-32B](https://huggingface.co/BAAI/RoboBrain2.0-32B)   | 32B parameter version of the RoboBrain2.0 |
+
+## 🛠️ Setup
+
+```bash
+# clone repo.
+git clone https://github.com/FlagOpen/RoboBrain2.0.git
+cd RoboBrain
+
+# build conda env.
+conda create -n robobrain2 python=3.10
+conda activate robobrain2
+pip install -r requirements.txt
+```
+
+
+## 🤖 Simple Inference
+**Note: Please refer to [RoboBrain 2.0 Github](https://github.com/FlagOpen/RoboBrain2.0) for the usage of RoboBrain 2.0**
+
+
+## 😊 More Results
+
+**Benchmark comparison across spatial reasoning and temporal task planning.** ***RoboBrain2.0*** achieves state-of-the-art (SOTA) or near-SOTA performance on nine spatial reasoning benchmarks: ***BLINK-Spatial***, ***CV-Bench***, ***EmbSpatial***, ***RoboSpatial***, ***RefSpatial***, ***SAT***, ***VSI-Bench***, ***Where2Place*** and ***ShareRobot-Bench***, and three temporal reasoning benchmarks: ***Multi-Robot-Planning***, ***Ego-Plan2*** and ***RoboBench-Planning***,  It not only outperforms leading open-source models such as Cosmos-Reason1 and Qwen2.5-VL, but also surpasses closed-source models like Gemini 2.5 Pro, o4-mini and Claude Sonnet 4.
+
+<div align="center">
+<img src="https://github.com/FlagOpen/RoboBrain2.0/raw/main/assets/result_table_1.png" />
+</div>
+
+<div align="center">
+<img src="https://github.com/FlagOpen/RoboBrain2.0/raw/main/assets/result_table_2.png" />
+</div>
+
+<div align="center">
+<img src="https://github.com/FlagOpen/RoboBrain2.0/raw/main/assets/result_table_3.png" />
+</div>
+
+
+## 📑 Citation
+If you find this project useful, welcome to cite us.
+```bib
+@article{RoboBrain 2.0,
+    title={RoboBrain 2.0 Technical Report},
+    author={BAAI RoboBrain Team},
+    journal={arXiv preprint arXiv:2507.02029},
+    year={2025}
+}
+
+@article{RoboBrain 1.0,
+    title={Robobrain: A unified brain model for robotic manipulation from abstract to concrete},
+    author={Ji, Yuheng and Tan, Huajie and Shi, Jiayu and Hao, Xiaoshuai and Zhang, Yuan and Zhang, Hengyuan and Wang, Pengwei and Zhao, Mengdi and Mu, Yao and An, Pengju and others},
+    journal={arXiv preprint arXiv:2502.21257},
+    year={2025}
+}
+
+@article{RoboOS,
+    title={RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration},
+    author={Tan, Huajie and Hao, Xiaoshuai and Lin, Minglan and Wang, Pengwei and Lyu, Yaoxu and Cao, Mingyu and Wang, Zhongyuan and Zhang, Shanghang},
+    journal={arXiv preprint arXiv:2505.03673},
+    year={2025}
+}
+
+@article{zhou2025roborefer,
+    title={RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics},
+    author={Zhou, Enshen and An, Jingkun and Chi, Cheng and Han, Yi and Rong, Shanyu and Zhang, Chi and Wang, Pengwei and Wang, Zhongyuan and Huang, Tiejun and Sheng, Lu and others},
+    journal={arXiv preprint arXiv:2506.04308},
+    year={2025}
+}
+
+@article{Reason-RFT,
+    title={Reason-rft: Reinforcement fine-tuning for visual reasoning},
+    author={Tan, Huajie and Ji, Yuheng and Hao, Xiaoshuai and Lin, Minglan and Wang, Pengwei and Wang, Zhongyuan and Zhang, Shanghang},
+    journal={arXiv preprint arXiv:2503.20752},
+    year={2025}
+}
+
+@article{Code-as-Monitor,
+    title={Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection},
+    author={Zhou, Enshen and Su, Qi and Chi, Cheng and Zhang, Zhizheng and Wang, Zhongyuan and Huang, Tiejun and Sheng, Lu and Wang, He},
+    journal={arXiv preprint arXiv:2412.04455},
+    year={2024}
+}
+```

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BAAI-RoboBrain2.0-7B/preprocessor_config.json

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+  "processor_class": "Qwen2_5_VLProcessor"
+}

BAAI-RoboBrain2.0-7B/tokenizer_config.json

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+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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BAAI-RoboBrain2.0-7B_budgetthinker_3ratio/added_tokens.json

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BAAI-RoboBrain2.0-7B_budgetthinker_3ratio/config.json

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BAAI-RoboBrain2.0-7B_budgetthinker_3ratio/generation_config.json

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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+    },
+    "151665": {
+      "content": "\n<remaining>1/3</remaining>\n",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151666": {
+      "content": "\n<remaining>2/3</remaining>\n",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "\n<remaining>1/3</remaining>\n",
+    "\n<remaining>2/3</remaining>\n"
+  ],
+  "bos_token": null,
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

add_special_tokens_3ratio.py

@@ -0,0 +1,26 @@
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer
+import torch
+
+if __name__ == "__main__":
+    # 加载原始模型和tokenizer
+    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+        "/mnt/lyc/wuxinrui/RoboBrain-2/BAAI-RoboBrain2.0-7B",
+        torch_dtype=torch.bfloat16  # 保持精度一致
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        "/mnt/lyc/wuxinrui/RoboBrain-2/BAAI-RoboBrain2.0-7B"
+    )
+
+    # 添加特殊token并调整模型权重
+    bins_tokens = [f"\n<remaining>{i+1}/3</remaining>\n" for i in range(2)]
+    tokenizer.add_special_tokens({'additional_special_tokens': bins_tokens})
+    # model.resize_token_embeddings(len(tokenizer))  # 调整所有相关权重
+
+    # 保存完整模型
+    NEW_MODEL = '/mnt/lyc/wuxinrui/RoboBrain-2/BAAI-RoboBrain2.0-7B_budgetthinker_3ratio'
+    model.save_pretrained(NEW_MODEL, safe_serialization=True)
+    tokenizer.save_pretrained(NEW_MODEL)
+
+    # 验证加载
+    loaded_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(NEW_MODEL)
+    print("验证词汇表大小:", len(loaded_model.get_input_embeddings().weight))

robobrain_7b_abalation_SFT_v2_2epoch/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+models/tokenizer.json filter=lfs diff=lfs merge=lfs -text

robobrain_7b_abalation_SFT_v2_2epoch/models/README.md

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+---
+library_name: transformers
+license: other
+base_model: /data2/wuxinrui/RoboBrain2.0/HF_Models/BAAI-RoboBrain2.0-7B
+tags:
+- llama-factory
+- full
+- generated_from_trainer
+model-index:
+- name: output_1
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# output_1
+
+This model is a fine-tuned version of [/data2/wuxinrui/RoboBrain2.0/HF_Models/BAAI-RoboBrain2.0-7B](https://huggingface.co//data2/wuxinrui/RoboBrain2.0/HF_Models/BAAI-RoboBrain2.0-7B) on the COT_1_shorten_shorten2_budgetthinker_abalation_mllm, the COT_1_shorten_budgetthinker_abalation_mllm, the COT_1_budgetthinker_abalation_mllm, the COT_2_shorten_shorten2_budgetthinker_abalation_mllm, the COT_2_shorten_budgetthinker_abalation_mllm, the COT_2_budgetthinker_abalation_mllm, the COT_3_shorten_shorten2_budgetthinker_abalation_mllm, the COT_3_shorten_budgetthinker_abalation_mllm, the COT_3_budgetthinker_abalation_mllm, the COT_4_shorten_shorten2_budgetthinker_abalation_mllm, the COT_4_shorten_budgetthinker_abalation_mllm, the COT_4_budgetthinker_abalation_mllm, the COT_5_shorten_shorten2_budgetthinker_abalation_mllm, the COT_5_shorten_budgetthinker_abalation_mllm, the COT_5_budgetthinker_abalation_mllm, the COT_6_shorten_shorten2_budgetthinker_abalation_mllm, the COT_6_shorten_budgetthinker_abalation_mllm, the COT_6_budgetthinker_abalation_mllm, the COT_8_shorten_shorten2_budgetthinker_abalation_mllm, the COT_8_shorten_budgetthinker_abalation_mllm, the COT_8_budgetthinker_abalation_mllm, the COT_9_shorten_shorten2_budgetthinker_abalation_mllm, the COT_9_shorten_budgetthinker_abalation_mllm, the COT_9_budgetthinker_abalation_mllm, the COT_10_shorten_shorten2_budgetthinker_abalation_mllm, the COT_10_shorten_budgetthinker_abalation_mllm and the COT_10_budgetthinker_abalation_mllm datasets.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-06
+- train_batch_size: 2
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 6
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 24
+- total_eval_batch_size: 48
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 2.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.51.3
+- Pytorch 2.8.0+cu128
+- Datasets 3.6.0
+- Tokenizers 0.21.1

robobrain_7b_abalation_SFT_v2_2epoch/models/added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
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+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}