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Browse files- README.md +35 -184
- config.json +1 -1
- generation_config.json +3 -2
- model-00001-of-00003.safetensors +2 -2
- model-00002-of-00003.safetensors +2 -2
- model-00003-of-00003.safetensors +2 -2
- trainer_state.json +0 -0
- training_args.bin +3 -0
README.md
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library_name:
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#
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- 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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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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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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**BibTeX:**
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**APA:**
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## Glossary [optional]
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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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[More Information Needed]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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library_name: sglang
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inference: false
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# Inference Preparation
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This is a fork of liuhaotian/llava-v1.6-vicuna-7b to be fully compatible for inference with
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[SGLang](https://github.com/sgl-project/sglang/). No other changes were made.
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<br>
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<br>
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# LLaVA Model Card
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## Model details
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**Model type:**
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LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.
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It is an auto-regressive language model, based on the transformer architecture.
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Base LLM: [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5)
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**Model date:**
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LLaVA-v1.6-Vicuna-7B was trained in December 2023.
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**Paper or resources for more information:**
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https://llava-vl.github.io/
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## License
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Llama 2 is licensed under the LLAMA 2 Community License,
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Copyright (c) Meta Platforms, Inc. All Rights Reserved.
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**Where to send questions or comments about the model:**
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https://github.com/haotian-liu/LLaVA/issues
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## Intended use
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**Primary intended uses:**
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The primary use of LLaVA is research on large multimodal models and chatbots.
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**Primary intended users:**
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The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
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## Training dataset
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- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
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- 158K GPT-generated multimodal instruction-following data.
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- 500K academic-task-oriented VQA data mixture.
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- 50K GPT-4V data mixture.
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- 40K ShareGPT data.
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## Evaluation dataset
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A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.
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config.json
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pad_token_id":
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"pretraining_tp": 1,
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"projector_hidden_act": "gelu",
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"rms_norm_eps": 1e-05,
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"projector_hidden_act": "gelu",
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"rms_norm_eps": 1e-05,
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"transformers_version": "4.
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"attn_implementation": "flash_attention_2",
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"bos_token_id": 1,
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"eos_token_id": 2,
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"max_length": 4096,
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"transformers_version": "4.36.2"
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}
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trainer_state.json
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training_args.bin
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