Yuxuan-Qiao commited on
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README.md CHANGED
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  ---
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  license: cc-by-4.0
 
 
 
 
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  ---
 
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  ---
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  license: cc-by-4.0
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+ datasets:
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+ - FreedomIntelligence/ALLaVA-4V
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+ pipeline_tag: image-text-to-text
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+ library_name: prismcaptioner
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  ---
llm_adapter/README.md ADDED
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+ ---
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+ library_name: peft
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+ base_model: internlm/internlm2-chat-1_8b
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+
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+
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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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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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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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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+
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+ ### Direct Use
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+
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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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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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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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+
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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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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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- 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. -->
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+
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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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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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+
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+ #### Training Hyperparameters
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+
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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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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+
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+ [More Information Needed]
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+
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+ <!-- This should link to a Dataset Card if possible. -->
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+
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+ [More Information Needed]
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+
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+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+ [More Information Needed]
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+
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+ ### Results
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+
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+ [More Information Needed]
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+
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+ #### Summary
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+
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+
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+
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+ ## Model Examination [optional]
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+
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+ <!-- Relevant interpretability work for the model goes here -->
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+
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+ [More Information Needed]
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+
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+ ## Environmental Impact
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+
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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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+
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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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+
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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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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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+ #### Software
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+
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+ [More Information Needed]
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+
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+ ## Citation [optional]
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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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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ ## Glossary [optional]
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+
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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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+
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+ [More Information Needed]
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+
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+ ## More Information [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Authors [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Contact
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+
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+ [More Information Needed]
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+ ### Framework versions
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+
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+ - PEFT 0.9.0
llm_adapter/adapter_config.json ADDED
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+ {
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+ "alpha_pattern": {},
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+ "auto_mapping": null,
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+ "base_model_name_or_path": "internlm/internlm2-chat-1_8b",
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+ "bias": "none",
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+ "fan_in_fan_out": false,
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+ "inference_mode": true,
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+ "init_lora_weights": true,
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+ "layers_pattern": null,
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+ "layers_to_transform": null,
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+ "loftq_config": {},
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+ "lora_alpha": 256,
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+ "lora_dropout": 0.05,
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+ "megatron_config": null,
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+ "megatron_core": "megatron.core",
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+ "modules_to_save": null,
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+ "peft_type": "LORA",
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+ "r": 512,
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+ "rank_pattern": {},
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+ "revision": null,
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+ "target_modules": [
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+ "wo",
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+ "w3",
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+ "w2",
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+ "wqkv",
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+ "output",
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+ "w1"
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+ ],
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+ "task_type": "CAUSAL_LM",
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+ "use_dora": false,
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+ "use_rslora": false
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+ }
llm_adapter/adapter_model.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:054ac799b9fdfe7a9d0280a1a47ab520457ef35e8b5690d46666657e0da10ab2
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+ size 1103527968
projector/config.json ADDED
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+ {
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+ "architectures": [
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+ "ProjectorModel"
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+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_projector.ProjectorConfig",
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+ "AutoModel": "modeling_projector.ProjectorModel"
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+ },
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+ "bias": true,
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+ "depth": 2,
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+ "hidden_act": "gelu",
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+ "llm_hidden_size": 2048,
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+ "model_type": "projector",
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.40.0",
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+ "visual_hidden_size": 1152
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+ }
projector/configuration_projector.py ADDED
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+ # Copyright (c) OpenMMLab. All rights reserved.
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+ from transformers import PretrainedConfig
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+
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+
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+ class ProjectorConfig(PretrainedConfig):
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+ model_type = 'projector'
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+ _auto_class = 'AutoConfig'
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+
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+ def __init__(
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+ self,
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+ visual_hidden_size=4096,
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+ llm_hidden_size=4096,
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+ depth=2,
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+ hidden_act='gelu',
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+ bias=True,
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+ **kwargs,
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+ ):
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+ self.visual_hidden_size = visual_hidden_size
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+ self.llm_hidden_size = llm_hidden_size
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+ self.depth = depth
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+ self.hidden_act = hidden_act
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+ self.bias = bias
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+ super().__init__(**kwargs)
projector/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:96066ac3bc6abbf7f2c1bd380b39317cec788e48ba9e5ac286dc61bd8c59d98d
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+ size 13115752
projector/modeling_projector.py ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Copyright (c) OpenMMLab. All rights reserved.
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+ import torch
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+ import torch.nn as nn
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+ from transformers import PreTrainedModel
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+ from transformers.activations import ACT2FN
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+
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+ from .configuration_projector import ProjectorConfig
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+
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+
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+ class ProjectorModel(PreTrainedModel):
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+ _auto_class = 'AutoModel'
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+ config_class = ProjectorConfig
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+ base_model_prefix = 'model'
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+ supports_gradient_checkpointing = True
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+
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+ def __init__(self, config: ProjectorConfig) -> None:
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+ super().__init__(config)
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+ self.gradient_checkpointing = False
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+
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+ modules = [
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+ nn.Linear(
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+ config.visual_hidden_size,
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+ config.llm_hidden_size,
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+ bias=config.bias)
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+ ]
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+ for _ in range(1, config.depth):
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+ modules.append(ACT2FN[config.hidden_act])
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+ modules.append(
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+ nn.Linear(
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+ config.llm_hidden_size,
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+ config.llm_hidden_size,
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+ bias=config.bias))
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+ self.model = nn.Sequential(*modules)
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+
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+ def enable_input_require_grads(self):
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+
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+ def make_inputs_require_grad(module, input, output):
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+ output.requires_grad_(True)
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+
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+ self.model.register_forward_hook(make_inputs_require_grad)
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+
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+ def _set_gradient_checkpointing(self, module, value=False):
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+ if isinstance(module, ProjectorModel):
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+ module.gradient_checkpointing = value
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+
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+ def forward(self, x):
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+ if self.gradient_checkpointing and self.training:
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+ layer_outputs = torch.utils.checkpoint.checkpoint(self.model, x)
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+ else:
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+ layer_outputs = self.model(x)
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+ return layer_outputs