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Duplicate from HuggingFaceH4/starcoderbase-finetuned-oasst1

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README.md ADDED
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+ ---
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+ license: bigcode-openrail-m
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+ duplicated_from: HuggingFaceH4/starcoderbase-finetuned-oasst1
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+ ---
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+
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+ # Model Card for starcoderbase-finetuned-oasst1
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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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+ This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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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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+ - **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 Data 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 Data 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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+ [More Information Needed]
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+ ## Model Card Contact
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+ [More Information Needed]
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+
TRAINER_README.md ADDED
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+ ---
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: starcoder-ift
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # starcoder-ift
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+
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+ This model is a fine-tuned version of [bigcode/large-model](https://huggingface.co/bigcode/large-model) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.4943
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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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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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - num_devices: 8
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 128
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+ - total_eval_batch_size: 16
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.03
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+ - num_epochs: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 1.6668 | 0.99 | 65 | 1.6167 |
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+ | 1.3584 | 2.0 | 131 | 1.5126 |
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+ | 1.0949 | 2.98 | 195 | 1.4943 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.28.1
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+ - Pytorch 1.13.1+cu117
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.3
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+ {
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+ "epoch": 2.98,
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+ "eval_loss": 1.4942539930343628,
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+ "eval_runtime": 22.3723,
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+ "eval_samples": 879,
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+ "eval_samples_per_second": 39.29,
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+ "eval_steps_per_second": 2.458,
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+ "perplexity": 4.456011097278015,
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+ "train_loss": 1.4244933299529248,
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+ "train_runtime": 3089.4842,
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+ "train_samples": 8372,
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+ "train_samples_per_second": 8.13,
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+ "train_steps_per_second": 0.063
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+ }
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+ {
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+ "_name_or_path": "/fsx/h4/checkpoints/starchat",
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+ "activation_function": "gelu",
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+ "architectures": [
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+ "GPTBigCodeForCausalLM"
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+ ],
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+ "attention_softmax_in_fp32": true,
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+ "model_type": "gpt_bigcode",
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+ "multi_query": true,
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+ "n_embd": 6144,
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+ "n_head": 48,
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+ "n_inner": 24576,
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+ "n_layer": 40,
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+ "n_positions": 8192,
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+ "pad_key_length": true,
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+ "pre_allocate_kv_cache": false,
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+ "resid_pdrop": 0.1,
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+ "scale_attention_softmax_in_fp32": true,
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+ "scale_attn_weights": true,
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+ "summary_activation": null,
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+ "summary_first_dropout": 0.1,
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+ "summary_proj_to_labels": true,
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+ "summary_type": "cls_index",
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+ "summary_use_proj": true,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.28.1",
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+ "use_cache": false,
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+ "validate_runner_input": true,
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+ "vocab_size": 49156
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+ }
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+ {
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+ "assistant_token": "<|assistant|>",
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+ "end_token": "<|end|>"
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+ }
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+ {
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+ "epoch": 2.98,
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+ "eval_loss": 1.4942539930343628,
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+ "eval_runtime": 22.3723,
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+ "eval_samples": 879,
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+ "eval_samples_per_second": 39.29,
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+ "eval_steps_per_second": 2.458,
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+ "perplexity": 4.456011097278015
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+ }
generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 0,
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+ "eos_token_id": 0,
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+ "transformers_version": "4.28.1"
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+ }
handler.py ADDED
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+ from typing import Any, Dict
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+
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+ import torch
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ from peft import PeftConfig, PeftModel
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+
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+
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+ class EndpointHandler:
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+ def __init__(self, path=""):
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+ # load model and processor from path
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+ self.tokenizer = AutoTokenizer.from_pretrained(path)
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+ try:
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+ config = PeftConfig.from_pretrained(path)
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+ model = AutoModelForCausalLM.from_pretrained(
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+ config.base_model_name_or_path,
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+ return_dict=True,
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+ load_in_8bit=True,
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+ device_map="auto",
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+ torch_dtype=torch.float16,
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+ )
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+ model.resize_token_embeddings(len(self.tokenizer))
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+ model = PeftModel.from_pretrained(model, path)
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+ except Exception:
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+ model = AutoModelForCausalLM.from_pretrained(
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+ path,
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+ device_map="auto",
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+ load_in_8bit=True,
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+ torch_dtype=torch.float16,
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+ )
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+ self.model = model
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+ self.device = "cuda" if torch.cuda.is_available() else "cpu"
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+
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+ def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
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+ # process input
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+ inputs = data.pop("inputs", data)
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+ parameters = data.pop("parameters", None)
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+
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+ # preprocess
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+ inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)
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+
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+ # pass inputs with all kwargs in data
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+ if parameters is not None:
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+ outputs = self.model.generate(**inputs, **parameters)
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+ else:
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+ outputs = self.model.generate(**inputs)
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+
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+ # postprocess the prediction
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+ prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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+
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+ return [{"generated_text": prediction}]
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