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.gitattributes

@@ -57,3 +57,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+VTimeLLM/data/activitynet/mdpo-train.json filter=lfs diff=lfs merge=lfs -text
+VTimeLLM/flash-attention/assets/flashattn_banner.pdf filter=lfs diff=lfs merge=lfs -text
+VTimeLLM/flash-attention/flash_attn-2.8.3+cu12torch2.8cxx11abiTRUE-cp310-cp310-linux_x86_64.whl filter=lfs diff=lfs merge=lfs -text

VTimeLLM/.vscode/launch.json

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+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Debug MDPO Training",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/train/debug_mdpo_train.sh",
+            "console": "integratedTerminal",
+            "cwd": "${workspaceFolder}",
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}:${env:PYTHONPATH}",
+                "CUDA_VISIBLE_DEVICES": "1",
+                "TRANSFORMERS_OFFLINE": "1",
+                "WANDB_PROJECT": "vtimellm"
+            },
+            "args": [],
+            "justMyCode": false,
+            "stopOnEntry": false
+        },
+        {
+            "name": "Debug Python Training Direct",
+            "type": "python",
+            "request": "launch",
+            "module": "vtimellm.train.train_dpo_mem",
+            "console": "integratedTerminal",
+            "cwd": "${workspaceFolder}",
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}:${env:PYTHONPATH}",
+                "CUDA_VISIBLE_DEVICES": "1",
+                "TRANSFORMERS_OFFLINE": "1",
+                "WANDB_PROJECT": "vtimellm",
+                "RANK": "1",
+                "MASTER_PORT": "29571"
+            },
+            "args": [
+                "--deepspeed", "./scripts/zero2.json",
+                "--lora_enable", "True",
+                "--lora_r", "8",
+                "--lora_alpha", "128",
+                "--training_stage", "3",
+                "--finetuning", "True",
+                "--model_name_or_path", "./checkpoints/vicuna-7b-v1.5",
+                "--version", "v1",
+                "--data_path", "./data/activitynet/mdpo-train.json",
+                "--data_folder", "./data/activitynet/videos/train",
+                "--feat_folder", "./data/activitynet/clipvitl14-vtimellm.pth",
+                "--pretrain_mm_mlp_adapter", "./checkpoints/vtimellm-vicuna-v1-5-7b-stage1/mm_projector.bin",
+                "--stage2_path", "./checkpoints/vtimellm-vicuna-v1-5-7b-stage2",
+                "--stage3_path", "./checkpoints/vtimellm-vicuna-v1-5-7b-stage3",
+                "--stage4_path", "checkpoints/vtimellm-vicuna-v1-5-7b-activitynet-stage4",
+                "--output_dir", "./outputs/vtimellm-vicuna-v1-5-7b-activitynet-stage5",
+                "--bf16", "True",
+                "--max_steps", "100",
+                "--per_device_train_batch_size", "2",
+                "--gradient_accumulation_steps", "4",
+                "--evaluation_strategy", "no",
+                "--save_strategy", "no",
+                "--save_steps", "50000",
+                "--save_total_limit", "10",
+                "--learning_rate", "1e-6",
+                "--freeze_mm_mlp_adapter", "True",
+                "--weight_decay", "0.",
+                "--warmup_ratio", "0.1",
+                "--lr_scheduler_type", "cosine",
+                "--logging_steps", "1",
+                "--tf32", "True",
+                "--model_max_length", "2048",
+                "--gradient_checkpointing", "True",
+                "--dataloader_num_workers", "4",
+                "--lazy_preprocess", "True",
+                "--report_to", "none",
+                "--run_name", "vtimellm-vicuna-v1-5-7b-activitynet-stage5",
+                "--gamma", "0.0",
+                "--beta", "0.5",
+                "--dpo_alpha", "1.0",
+                "--train4dpo"
+            ],
+            "justMyCode": false,
+            "stopOnEntry": false
+        },
+        {
+            "name": "Debug with DeepSpeed",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/train/debug_deepspeed.py",
+            "console": "integratedTerminal",
+            "cwd": "${workspaceFolder}",
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}:${env:PYTHONPATH}",
+                "CUDA_VISIBLE_DEVICES": "1",
+                "TRANSFORMERS_OFFLINE": "1",
+                "WANDB_PROJECT": "vtimellm",
+                "RANK": "1",
+                "MASTER_PORT": "29571"
+            },
+            "args": [],
+            "justMyCode": false,
+            "stopOnEntry": false
+        }
+    ]
+}

VTimeLLM/.vscode/settings.json

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+{
+    "python.defaultInterpreterPath": "/home/jisoo/.conda/envs/vtimellm/bin/python",
+    "python.terminal.activateEnvironment": true,
+    "python.condaPath": "/home/jisoo/.conda/bin/conda",
+    "python.envFile": "${workspaceFolder}/.env",
+    "python.analysis.extraPaths": [
+        "${workspaceFolder}",
+        "${workspaceFolder}/vtimellm",
+        "${workspaceFolder}/vtimellm/train",
+        "${workspaceFolder}/vtimellm/model"
+    ],
+    "python.analysis.autoImportCompletions": true,
+    "python.analysis.typeCheckingMode": "basic",
+    "python.analysis.autoSearchPaths": true,
+    "python.analysis.diagnosticMode": "workspace",
+    "python.analysis.stubPath": "${workspaceFolder}/typings",
+    "python.linting.enabled": true,
+    "python.linting.pylintEnabled": false,
+    "python.linting.flake8Enabled": true,
+    "python.linting.mypyEnabled": false,
+    "python.formatting.provider": "black",
+    "python.sortImports.args": ["--profile", "black"],
+    "files.associations": {
+        "*.py": "python"
+    },
+    "terminal.integrated.env.linux": {
+        "PYTHONPATH": "${workspaceFolder}:${env:PYTHONPATH}",
+        "CUDA_VISIBLE_DEVICES": "1",
+        "TRANSFORMERS_OFFLINE": "1",
+        "WANDB_PROJECT": "vtimellm"
+    },
+    "terminal.integrated.defaultProfile.linux": "bash",
+    "terminal.integrated.profiles.linux": {
+        "bash": {
+            "path": "/bin/bash",
+            "args": ["-l"]
+        }
+    }
+}

VTimeLLM/checkpoints/ViT-L-14.pt

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VTimeLLM/checkpoints/vicuna-7b-v1.5/.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
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+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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VTimeLLM/checkpoints/vicuna-7b-v1.5/README.md

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+---
+inference: false
+license: llama2
+---
+
+# Vicuna Model Card
+
+## Model Details
+
+Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.
+
+- **Developed by:** [LMSYS](https://lmsys.org/)
+- **Model type:** An auto-regressive language model based on the transformer architecture
+- **License:** Llama 2 Community License Agreement	
+- **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288)
+
+### Model Sources
+
+- **Repository:** https://github.com/lm-sys/FastChat
+- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/
+- **Paper:** https://arxiv.org/abs/2306.05685
+- **Demo:** https://chat.lmsys.org/
+
+## Uses
+
+The primary use of Vicuna is research on large language models and chatbots.
+The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
+
+## How to Get Started with the Model
+
+- Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights
+- APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api  
+
+## Training Details
+
+Vicuna v1.5 is fine-tuned from Llama 2 with supervised instruction fine-tuning.
+The training data is around 125K conversations collected from ShareGPT.com.
+See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).
+
+## Evaluation
+
+![Evaluation Results](https://github.com/lm-sys/lm-sys.github.io/blob/main/public/images/webdata/vicuna_v1.5_eval.png?raw=true)
+
+Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
+
+## Difference between different versions of Vicuna
+
+See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)

VTimeLLM/checkpoints/vicuna-7b-v1.5/config.json

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+  ],
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+  "rope_scaling": null,
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+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 32000
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VTimeLLM/checkpoints/vicuna-7b-v1.5/generation_config.json

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+---
+base_model: ./checkpoints/vtimellm/vicuna-7b-v1.5
+library_name: peft
+---
+
+# 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. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
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+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
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+- **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. -->
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+[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 -->
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+### 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
+
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+
+<!-- This should link to a Dataset Card if possible. -->
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+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
+#### Metrics
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+<!-- Relevant interpretability work for the model goes here -->
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+
+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).
+
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+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
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+## Model Card Contact
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+### Framework versions
+
+- PEFT 0.13.2

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VTimeLLM/checkpoints/vtimellm-vicuna-v1-5-7b-activitynet-stage5/README.md

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+---
+base_model: ./checkpoints/vtimellm/vicuna-7b-v1.5
+library_name: peft
+---
+
+# 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. -->
+
+
+
+- **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]
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+- **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. -->
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+[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 -->
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+[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]
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+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
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+
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+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+## Model Card Authors [optional]
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+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.13.2

VTimeLLM/checkpoints/vtimellm-vicuna-v1-5-7b-activitynet-stage5/adapter_config.json

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+[email protected]: 61.41
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