| --- |
| library_name: transformers |
| license: mit |
| language: |
| - en |
| metrics: |
| - bleu |
| - code_eval |
| - rouge |
| - chrf |
| model_name: MicroCoderFIM-1B |
| base_model: bigcode/starcoderbase-1b |
| model-index: |
| - name: MicroCoderFIM-1B |
| results: |
| - task: |
| type: text-generation |
| dataset: |
| type: openai_humaneval |
| name: HumanEval |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 65.46 |
| verified: false |
| - name: pass@10 |
| type: pass@10 |
| value: 90.36 |
| verified: false |
| - name: pass@100 |
| type: pass@100 |
| value: 94.43 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: xvadov01/cpp_emb_nl2pl |
| name: xvadov01/cpp_emb_nl2pl |
| metrics: |
| - name: BLEU |
| type: bleu |
| value: 31.74 |
| verified: false |
| - name: codeBLEU |
| type: codeBLEU |
| value: 40.53 |
| verified: false |
| - name: chrf++ |
| type: chrf |
| value: 51.54 |
| verified: false |
| - name: rouge-l |
| type: rouge |
| value: 43.31 |
| verified: false |
| --- |
| |
| # Model Card for Model ID |
|
|
| This is a finetuned version of StarCoderBase 1B using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on [dataset](https://huggingface.co/datasets/xvadov01/cpp_emb_nl2pl) focused on embedded systems programming. |
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| ## 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] |
| - **Shared by [optional]:** [More Information Needed] |
| - **Model type:** Transformer decoder architecture with Multi-Query attention |
| - **Language(s) (NLP):** English |
| - **License:** MIT |
| - **Finetuned from model [optional]:** [StarCoderBase 1B](https://huggingface.co/bigcode/starcoderbase-1b) |
|
|
| ### Model Sources [optional] |
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| <!-- Provide the basic links for the model. --> |
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| - **Repository:** [More Information Needed] |
| - **Paper [optional]:** [More Information Needed] |
| - **Demo [optional]:** [More Information Needed] |
|
|
| ## Uses |
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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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| ### Out-of-Scope Use |
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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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| ### 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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| ## 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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| #### Preprocessing [optional] |
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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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| ## 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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| #### Factors |
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| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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| ### Results |
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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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| ## 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). |
|
|
| - **Hardware Type:** NVIDIA GeForce RTX 3090 |
| - **Hours used:** 5h 25m |
| - **Carbon Emitted:** 0.83 |
|
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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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| **BibTeX:** |
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| ## Glossary [optional] |
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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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