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--- |
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license: bigscience-bloom-rail-1.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- alpaca |
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- bloom |
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- LLM |
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datasets: |
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- tatsu-lab/alpaca |
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--- |
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# AlpacOOM: Alpaca 🦙 + BLOOM 💮 |
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## Adapter Description |
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This adapter was created with 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**. |
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## Model Description |
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BigScience Large Open-science Open-access Multilingual Language Model |
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[BLOOM 7B1](https://huggingface.co/bigscience/bloom-7b1) |
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## Training data |
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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. |
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The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications: |
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- The `text-davinci-003` engine to generate the instruction data instead of `davinci`. |
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- 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`. |
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- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation. |
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- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions. |
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- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct. |
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This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). |
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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). |
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### Supported Tasks and Leaderboards |
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The Alpaca dataset is designed for instruction training pre-trained language models. |
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### Training procedure |
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TBA |
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## How to use |
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```py |
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import torch |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
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peft_model_id = "mrm8488/Alpacoom" |
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config = PeftConfig.from_pretrained(peft_model_id) |
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model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-7b1") |
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model = PeftModel.from_pretrained(model, peft_model_id) |
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model.eval() |
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# Based on the inference code by `tloen/alpaca-lora` |
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def generate_prompt(instruction, input=None): |
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if input: |
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return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. |
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### Instruction: |
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{instruction} |
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### Input: |
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{input} |
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### Response:""" |
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else: |
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return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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### Instruction: |
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{instruction} |
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### Response:""" |
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def generate( |
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instruction, |
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input=None, |
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temperature=0.1, |
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top_p=0.75, |
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top_k=40, |
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num_beams=4, |
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**kwargs, |
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): |
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prompt = generate_prompt(instruction, input) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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input_ids = inputs["input_ids"].cuda() |
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generation_config = GenerationConfig( |
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temperature=temperature, |
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top_p=top_p, |
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top_k=top_k, |
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num_beams=num_beams, |
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**kwargs, |
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) |
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with torch.no_grad(): |
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generation_output = model.generate( |
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input_ids=input_ids, |
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generation_config=generation_config, |
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return_dict_in_generate=True, |
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output_scores=True, |
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max_new_tokens=256, |
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) |
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s = generation_output.sequences[0] |
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output = tokenizer.decode(s) |
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return output.split("### Response:")[1].strip().split("Below")[0] |
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instruction = "Tell me about alpacas" |
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print("Instruction:", instruction) |
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print("Response:", generate(instruction)) |
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``` |
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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 = { Alpacoom (Revision 874f989) }, |
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year = 2023, |
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url = { https://huggingface.co/mrm8488/Alpacoom }, |
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doi = { 10.57967/hf/0449 }, |
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publisher = { Hugging Face } |
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} |
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``` |
