Create README.md

README.md

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+---
+inference: false
+tags:
+- text-generation
+- opt
+
+license: other
+commercial: false
+---
+# OPT-IML
+
+## Model Description
+
+OPT-IML models are instruction-tuned versions of OPT. They are fine-tuned on 2000 NLP tasks from 8 existing public benchmarks. 
+OPT-IML models are significantly better than OPT model and demonstrate different generalization abilities on four different 
+evaluation benchmarks with diverse tasks and input formats – PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG.
+
+### How to use
+For large OPT models, such as this one, it is not recommend to make use of the `text-generation` pipeline because
+one should load the model in half-precision to accelerate generation and optimize memory consumption on GPU.
+It is recommended to directly call the [`generate`](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate)
+ method as follows:
+
+```python
+>>> from transformers import AutoModelForCausalLM, AutoTokenizer
+>>> import torch
+
+>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-iml-30b", torch_dtype=torch.float16).cuda()
+
+>>> # the fast tokenizer currently does not work correctly
+>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-iml-30b", use_fast=False)
+
+>>> prompt = "What is the color of a carrot?\nA:"
+
+>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
+
+>>> generated_ids = model.generate(input_ids)
+
+>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+```
+
+### Limitations and bias
+
+While OPT-IML models outperform baseline OPT on an extensive set of evaluations,
+nevertheless, they are susceptible to the various risks associated with using large language models
+relating to factual correctness, generation of toxic language and enforcing stereotypes. While we release our
+OPT-IML models to proliferate future work on instruction-tuning and to improve the availability
+of large instruction-tuned causal LMs, the use of these models should be
+accompanied with responsible best practices.
+
+## Training data
+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. 
+
+## Training procedure
+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.
+
+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
+budget of OPT.
+
+
+### BibTeX entry and citation info
+```bibtex
+@misc{iyer2022opt,
+      title={OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization}, 
+      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},
+      year={2022},
+      eprint={2212.12017},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```