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--- |
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license: apache-2.0 |
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datasets: |
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- jtatman/python-code-dataset-500k |
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- jtatman/python-github-code-instruct-filtered-5k |
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- jtatman/pile_python_instruct_format |
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library_name: transformers |
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tags: |
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- code |
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--- |
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# Model Card for tinymistral-v2-pycoder-instruct-248m |
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This modelcard is for tinymistral-v2-pycoder-instruct, a python-specific code generation model on top of [Locutusque/TinyMistral-248M-v2-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2-Instruct). |
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## Model Details |
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This instruct model follows the original in using ChatML format. |
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An empty prompt will return various information from the base model, but using the instruct format will deliver python code of varying quality. |
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### Model Description |
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Model is in active development, base model is in active development, and all should be treated with caution. |
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- **Developed by:** [Locutusque and M4ai] |
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- **Funded by:** [Lint from a corner pocket] |
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- **Shared by:** [jtatman](https://huggingface.co/jtatman) |
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- **Model type:** [MistralForCausalLM](Locutusque/TinyMistral-248M-v2) |
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- **License:** [MIT] |
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- **Finetuned from model [Locutusque/TinyMistral-248M-v2](https://huggingface.co/Locutusque/TinyMistral-248M-v2-Instruct) |
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## Uses |
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Generate python code. |
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### Direct Use |
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Probably could be fine tuned with a more comprehensive dataset. Experiments are in progress. |
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## How to Get Started with the Model |
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Use the prompt format below to get started with the model. |
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<|im_start|>user |
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Write a function for multiplying two numbers, from variables 'a' and 'b'.<|im_end|> |
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<|im_start|>assistant |
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## Training Details |
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### Training Data |
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Custom formatted existing python data from: |
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- [jtatman/python-code-dataset-500k](https://huggingface.co/datasets/jtatman/python-code-dataset-500k) |
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- [jtatman/python-github-code-instruct-filtered-5k](https://huggingface.co/datasets/jtatman/python-github-code-instruct-filtered-5k) |
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- [jtatman/pile_python_instruct_format](https://huggingface.co/datasets/jtatman/pile_python_instruct_format) |
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### Training Procedure |
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Repeat training depending on compute budget. |
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#### Preprocessing |
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Conversion to alpaca/instruct format. |
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#### Training Hyperparameters |
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- **Training regime:** fp16, merge of parameter fine-tune adapters when necessary and helpful. |
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## Evaluation |
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#### Metrics |
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Latest metrics: |
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- epoch: 4.87 |
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- global_step: 220 |
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- learning_rate: 0.00006713780918727916 |
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- loss: 2.3736 |