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--- |
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license: cc-by-sa-3.0 |
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inference: false |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text2text-generation |
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datasets: |
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- pszemraj/dolly_hhrlhf-text2text |
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tags: |
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- instruct |
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- dolly_hhrlhf |
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--- |
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# bart-base-instruct: dolly_hhrlhf |
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<a href="https://colab.research.google.com/gist/pszemraj/a0c0a8cc24abfbf609f75f9d5c56c348/bart-base-instruct-example.ipynb"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the pszemraj/dolly_hhrlhf-text2text dataset. |
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## Model description |
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text2text models fine-tuned on a [modified dataset for text2text generation](https://huggingface.co/datasets/pszemraj/dolly_hhrlhf-text2text) based on the relatively more permissive [mosaicml/dolly_hhrlhf](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) dataset. |
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Basic usage in Python: |
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```python |
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# pip install -q transformers accelerate |
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from transformers import pipeline, GenerationConfig |
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model_name = "pszemraj/bart-base-instruct-dolly_hhrlhf" |
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assistant = pipeline( |
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"text2text-generation", |
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model_name, |
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device_map="auto" |
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) |
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cfg = GenerationConfig.from_pretrained(model_name) |
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# pass an 'instruction' as the prompt to the pipeline |
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prompt = "Write a guide on how to become a ninja while working a 9-5 job." |
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result = assistant(prompt, generation_config=cfg)[0]["generated_text"] |
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print(result) |
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``` |
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> using the generation config is optional, can subsitute with other generation params. |
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## Intended uses & limitations |
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- this is **not** tuned with RLHF etc, and may output offensive results |
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- this model is rather small (~600 MB) and therefore it's "cognition" abilities are rather limited. |
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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: 4e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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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_ratio: 0.03 |
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- num_epochs: 3.0 |
