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--- |
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license: openrail |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: santacoder-finetuned-the-stack-swift |
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results: [] |
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datasets: |
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- bigcode/the-stack-dedup |
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language: |
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- code |
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pipeline_tag: text-generation |
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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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# SantaCoder 🎅 fine-tuned on Swift 🍏 |
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This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8353 |
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## Model description |
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The [SantaCoder](https://huggingface.co/bigcode/santacoder) models are a series of 1.1B parameter models trained on the Python, Java, and JavaScript subset of [The Stack (v1.1)](https://huggingface.co/datasets/bigcode/the-stack) (which excluded opt-out requests). |
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The main model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255). |
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In addition, there are several models that were trained on datasets with different filter parameters and with architecture and objective variations. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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The Stack contains over 6TB of permissively-licensed source code files covering 358 programming languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. **This is the near-deduplicated version with 3TB data.** |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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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_steps: 100 |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 1.1132 | 0.05 | 500 | 1.0496 | |
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| 1.0077 | 0.1 | 1000 | 1.0245 | |
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| 1.0109 | 0.15 | 1500 | 1.0111 | |
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| 1.1106 | 0.2 | 2000 | 1.0025 | |
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| 0.5083 | 0.25 | 2500 | 1.0163 | |
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| 0.2996 | 0.3 | 3000 | 1.0339 | |
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| 1.0745 | 0.35 | 3500 | 0.9682 | |
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| 1.0355 | 0.4 | 4000 | 0.9467 | |
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| 0.9156 | 0.45 | 4500 | 0.9229 | |
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| 0.8834 | 0.5 | 5000 | 0.9199 | |
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| 0.6363 | 0.55 | 5500 | 0.9048 | |
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| 0.8771 | 0.6 | 6000 | 0.8899 | |
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| 1.9208 | 0.65 | 6500 | 0.8727 | |
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| 0.8816 | 0.7 | 7000 | 0.8633 | |
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| 0.8918 | 0.75 | 7500 | 0.8543 | |
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| 0.8714 | 0.8 | 8000 | 0.8454 | |
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| 0.9486 | 0.85 | 8500 | 0.8402 | |
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| 1.0609 | 0.9 | 9000 | 0.8364 | |
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| 0.9124 | 0.95 | 9500 | 0.8356 | |
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| 0.9743 | 1.0 | 10000 | 0.8353 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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### Citation |
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``` |
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@misc {manuel_romero_2023, |
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author = { {Manuel Romero} }, |
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title = { santacoder-finetuned-the-stack-swift (Revision 99b9470) }, |
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year = 2023, |
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url = { https://huggingface.co/mrm8488/santacoder-finetuned-the-stack-swift }, |
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doi = { 10.57967/hf/0348 }, |
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publisher = { Hugging Face } |
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} |
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``` |
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