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--- |
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license: mit |
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pipeline_tag: text2text-generation |
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tags: |
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- food |
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datasets: |
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- pocasrocas/recipe-gantt |
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language: |
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- en |
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--- |
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## Model Summary |
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recipe-gantt is a monotask language model to translate recipes into gantt charts in a TSV format. |
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It is a QLoRA finetune of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the [pocasrocas/recipe-gantt](https://huggingface.co/datasets/pocasrocas/recipe-gantt) dataset. I then used [llama.cpp](https://github.com/ggerganov/llama.cpp) to convert it to .gguf format for fast local inference. |
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## Intended use |
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To create recipe gantt charts and recipe gantt charts only 🧑🍳 |
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It's released here to accompany the [recipe-gantt tool](https://github.com/jbremz/recipe-gantt). |
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## Input format |
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The model was trained with the [alpaca](https://github.com/tatsu-lab/stanford_alpaca?tab=readme-ov-file#data-release) instruction/input/response prompt format. Exact details on the expected input can be inferred from the inference code [here](https://github.com/jbremz/recipe-gantt/blob/main/recipe-gantt.py). |
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## Limitations |
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- the model will inherit any limitations of the parent model (Mistral-7B-v0.1) |
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- it was finetuned on a tiny synthetic dataset of only 288 examples so sometimes produces corrupted TSV files and populates cells inaccurately. |
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## Training |
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- QLoRA finetune using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) |
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- ~1hr on NVIDIA GeForce RTX 3090 Ti ([wandb](https://wandb.ai/pocasrocas/recipe-gantt/runs/1ostj66y?workspace=user-pocasrocas)) |
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- Training code [here](https://github.com/jbremz/recipe-gantt/tree/720a7e1b3594c03b46209671271fe3c2159fff16/notebooks/002-train) |
