Filled model card

README.md

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