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--- |
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inference: false |
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tags: |
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- text-generation |
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- opt |
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license: other |
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commercial: false |
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--- |
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# OPT-IML |
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## Model Description |
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[OPT-IML (OPT + Instruction Meta-Learning)](https://arxiv.org/abs/2212.12017) is a set of instruction-tuned versions of OPT, on a collection of ~2000 NLP tasks gathered from 8 NLP benchmarks, called OPT-IML Bench. |
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We provide two model versions: |
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* OPT-IML trained on 1500 tasks with several tasks held-out for purposes of downstream evaluation, and |
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* OPT-IML-Max trained on all ~2000 tasks |
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### How to use |
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For large OPT models, such as this one, it is not recommend to make use of the `text-generation` pipeline because |
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one should load the model in half-precision to accelerate generation and optimize memory consumption on GPU. |
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It is recommended to directly call the [`generate`](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate) |
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method as follows: |
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```python |
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer |
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>>> import torch |
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>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-iml-30b", torch_dtype=torch.float16).cuda() |
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>>> # the fast tokenizer currently does not work correctly |
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>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-iml-30b", use_fast=False) |
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>>> prompt = "What is the color of a carrot?\nA:" |
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>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() |
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>>> generated_ids = model.generate(input_ids) |
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>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) |
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``` |
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### Limitations and bias |
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While OPT-IML models outperform baseline OPT on an extensive set of evaluations, |
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nevertheless, they are susceptible to the various risks associated with using large language models |
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relating to factual correctness, generation of toxic language and enforcing stereotypes. While we release our |
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OPT-IML models to proliferate future work on instruction-tuning and to improve the availability |
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of large instruction-tuned causal LMs, the use of these models should be |
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accompanied with responsible best practices. |
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## Training data |
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OPT-IML models are trained on OPT-IML Bench, a large benchmark for Instruction MetaLearning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks include Super-NaturalInstructions, FLAN, PromptSource, etc. |
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## Training procedure |
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The texts are tokenized using the GPT2 byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens. |
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The 30B model was fine-tuned on 64 40GB A100 GPUs. During fine-tuning, models saw approximately 2 billion tokens, which is only 0.6% of the pre-training |
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budget of OPT. |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{iyer2022opt, |
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title={OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization}, |
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author={Iyer, Srinivasan and Lin, Xi Victoria and Pasunuru, Ramakanth and Mihaylov, Todor and Simig, D{\'a}niel and Yu, Ping and Shuster, Kurt and Wang, Tianlu and Liu, Qing and Koura, Punit Singh and others}, |
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year={2022}, |
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eprint={2212.12017}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
