Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- alpaca
+- bloom
+- LLM
+---
+
+# AlpacOOM: Alpaca + BLOOM
+
+
+## Adapter Description
+This adapter was created by using the [PEFT](https://github.com/huggingface/peft) library and allowed the base model **BigScience/BLOOM 7B1** to be fine-tuned on the **Stanford's Alpaca Dataset** by using the method **LoRA**.
+
+## Model Description
+[BERTIN-GPT-J-6B](https://huggingface.co/bertin-project/bertin-gpt-j-6B) is a Spanish finetuned version of GPT-J 6B, a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
+
+## Training data
+Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
+
+The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:
+
+- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
+- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
+- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
+- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
+- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
+
+This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). 
+In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl).
+
+### Supported Tasks and Leaderboards
+
+The Alpaca dataset designed for instruction training pretrained language models.
+
+### Training procedure
+
+TBA
+
+## How to use
+```py
+
+```