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Model: facebook/opt-iml-max-30b Source: Original Platform
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<h2 align="center"> OPT-IML 175B LICENSE AGREEMENT </h2>
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Together with any copies of the Software Products (as well as derivative works thereof or works incorporating the Software Products) that you distribute, you must provide (i) a copy of this License, and (ii) the following attribution notice: “OPT-IML 175B is licensed under the OPT-175B license, Copyright (c) Meta Platforms, Inc. All Rights Reserved.”
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If any provision or part of a provision of this License is unlawful, void or unenforceable, that provision or part of the provision is deemed severed from this License, and will not affect the validity and enforceability of any remaining provisions. The failure of Meta to exercise or enforce any right or provision of this License will not operate as a waiver of such right or provision. This License does not confer any third-party beneficiary rights upon any other person or entity. This License, together with the Documentation, contains the entire understanding between you and Meta regarding the subject matter of this License, and supersedes all other written or oral agreements and understandings between you and Meta regarding such subject matter. No change or addition to any provision of this License will be binding unless it is in writing and signed by an authorized representative of both you and Meta.
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73
README.md
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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-max-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-max-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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```
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config.json
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{
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"_remove_final_layer_norm": false,
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"activation_function": "relu",
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"architectures": [
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"OPTForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"do_layer_norm_before": true,
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"dropout": 0.1,
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"eos_token_id": 2,
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"ffn_dim": 28672,
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"hidden_size": 7168,
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"init_std": 0.02,
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"layerdrop": 0.0,
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"max_position_embeddings": 2048,
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"model_type": "opt",
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"num_attention_heads": 56,
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"num_hidden_layers": 48,
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"pad_token_id": 1,
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"torch_dtype": "float16",
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"transformers_version": "4.22.2",
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"use_cache": true,
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"vocab_size": 50272,
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"word_embed_proj_dim": 7168
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}
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"pad_token_id": 1,
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"transformers_version": "4.27.0.dev0"
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}
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||||
1
special_tokens_map.json
Normal file
1
special_tokens_map.json
Normal file
@@ -0,0 +1 @@
|
||||
{"bos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}}
|
||||
1
tokenizer_config.json
Normal file
1
tokenizer_config.json
Normal file
@@ -0,0 +1 @@
|
||||
{"errors": "replace", "unk_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "add_bos_token": true, "special_tokens_map_file": null, "name_or_path": "facebook/opt-iml-30b"}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user