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108 lines
4.6 KiB
ReStructuredText
108 lines
4.6 KiB
ReStructuredText
..
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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GPT-J
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The GPT-J model was released in the `kingoflolz/mesh-transformer-jax
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<https://github.com/kingoflolz/mesh-transformer-jax>`__ repository by Ben Wang and Aran Komatsuzaki. It is a GPT-2-like
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causal language model trained on `the Pile <https://pile.eleuther.ai/>`__ dataset.
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This model was contributed by `Stella Biderman <https://huggingface.co/stellaathena>`__.
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Tips:
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- Running [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) in float32 precision on GPU requires at least 24 GB of
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RAM. On GPUs with less than 24 GB RAM, one should therefore load the model in half-precision:
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.. code-block::
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>>> from transformers import GPTJForCausalLM
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>>> import torch
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>>> model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16)
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- Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer. These extra
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tokens are added for the sake of efficiency on TPUs. To avoid the mis-match between embedding matrix size and vocab
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size, the tokenizer for [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) contains 143 extra tokens
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``<|extratoken_1|>... <|extratoken_143|>``, so the ``vocab_size`` of tokenizer also becomes 50400.
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Generation
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_______________________________________________________________________________________________________________________
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The :meth:`~transformers.generation_utils.GenerationMixin.generate` method can be used to generate text using GPT-J
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model.
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.. code-block::
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer
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>>> model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
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>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
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>>> prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
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... "previously unexplored valley, in the Andes Mountains. Even more surprising to the " \
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... "researchers was the fact that the unicorns spoke perfect English."
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>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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>>> gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=100,)
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>>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
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...or in float16 precision:
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.. code-block::
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>>> from transformers import GPTJForCausalLM, AutoTokenizer
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>>> import torch
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>>> model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16)
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>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
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>>> prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
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... "previously unexplored valley, in the Andes Mountains. Even more surprising to the " \
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... "researchers was the fact that the unicorns spoke perfect English."
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>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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>>> gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=100,)
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>>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
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GPTJConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPTJConfig
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:members:
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GPTJModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPTJModel
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:members: forward
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GPTJForCausalLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPTJForCausalLM
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:members: forward
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GPTJForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPTJForSequenceClassification
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:members: forward
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