Update README.md

README.md

@@ -1,71 +1,28 @@
 ---
-library_name: transformers
-tags: []
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# LLaVA Model Card
 
 ## 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]
+Model type: LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. 
 
-## Bias, Risks, and Limitations
+Base LLM: meta-llama/Meta-Llama-3-8B-Instruct
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Model Description
 
-[More Information Needed]
+**Repository:** https://github.com/EvolvingLMMs-Lab/LLaVA-NEXT
 
-### Recommendations
+**Primary intended uses:** The primary use of LLaVA is research on large multimodal models and chatbots.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+**Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+### License Notices
+  
+This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses, including but not limited to the OpenAI Terms of Use for the dataset and the specific licenses for base language models for checkpoints trained using the dataset (e.g. Llama-1/2 community license for LLaMA-2 and Vicuna-v1.5, Tongyi Qianwen RESEARCH LICENSE AGREEMENT and Llama-3 Research License). This project does not impose any additional constraints beyond those stipulated in the original licenses. Furthermore, users are reminded to ensure that their use of the dataset and checkpoints is in compliance with all applicable laws and regulations.
 
 ## How to Get Started with the Model
 
@@ -75,125 +32,90 @@ Use the code below to get started with the model.
 
 ## 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]
-
+We conducted the training on LLaVA-1.6's codebase with adding support of Llama-3 and Qwen model.
+
+### Training Hyperparameters
+
+```shell
+LLM_VERSION="meta-llama/Meta-Llama-3-8B-Instruct"
+LLM_VERSION_CLEAN="${LLM_VERSION//\//_}"
+VISION_MODEL_VERSION="openai/clip-vit-large-patch14-336"
+VISION_MODEL_VERSION_CLEAN="${VISION_MODEL_VERSION//\//_}"
+
+PROMPT_VERSION=plain
+PRETRAIN_DATA_VERSION="blip558k"
+############### Pretrain ################
+
+BASE_RUN_NAME="llavanext-${LLM_VERSION_CLEAN}-${VISION_MODEL_VERSION_CLEAN}-pretrain_${PRETRAIN_DATA_VERSION}_plain"
+echo "BASE_RUN_NAME: ${BASE_RUN_NAME}"
+
+PROMPT_VERSION="llava_llama_3"
+MID_RUN_NAME="llavanext-${VISION_MODEL_VERSION_CLEAN}-${LLM_VERSION_CLEAN}-blip558k_pretrain_plain_la_1_6mix_ft"
+echo "MID_RUN_NAME: ${MID_RUN_NAME}"
+
+torchrun # with necessary torchrun information for distributed training\
+    llava/train/train_mem.py \
+    --deepspeed scripts/zero3.json \
+    --model_name_or_path $LLM_VERSION \
+    --version $PROMPT_VERSION \
+    --data_path="/path/to/data/llava_instruct/llava1_6mix.json" \
+    --image_folder /path/to/data/llava_data \
+    --pretrain_mm_mlp_adapter="./checkpoints/projectors/${BASE_RUN_NAME}/mm_projector.bin" \
+    --mm_tunable_parts="mm_vision_tower,mm_mlp_adapter,mm_language_model" \
+    --mm_vision_tower_lr=2e-6 \
+    --vision_tower ${VISION_MODEL_VERSION} \
+    --mm_projector_type mlp2x_gelu \
+    --mm_vision_select_layer -2 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --group_by_modality_length True \
+    --image_aspect_ratio anyres \
+    --image_grid_pinpoints "[(336, 672), (672, 336), (672, 672), (1008, 336), (336, 1008)]" \
+    --mm_patch_merge_type spatial_unpad \
+    --bf16 True \
+    --run_name $MID_RUN_NAME \
+    --output_dir "./checkpoints/${MID_RUN_NAME}" \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 4 \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 3000 \
+    --save_total_limit 1 \
+    --learning_rate 1e-5 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 8192 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 16 \
+    --lazy_preprocess True \
+    --report_to wandb \
+    --torch_compile True \
+    --torch_compile_backend "inductor" \
+    --dataloader_drop_last True
+
+```
 
-#### Training Hyperparameters
+### Training Data
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
+- 158K GPT-generated multimodal instruction-following data.
+- 500K academic-task-oriented VQA data mixture.
+- 50K GPT-4V data mixture.
+- 40K ShareGPT data.
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+The training cost is ~15-20 hours on 2 x 8 NVIDIA A100-SXM4-80GB (may vary due to hardware differences). 
 
 [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]
+The evaluation is conducted with the support of [`lmms-eval`](https://github.com/EvolvingLMMs-Lab/lmms-eval)