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Eric Hallahan
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README.md
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README.md
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- causal-lm
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license: apache-2.0
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datasets:
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- the Pile
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- The Pile
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---
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# GPT-J 6B
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## Model Description
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GPT-J 6B is a transformer model designed using Ben Wang's [Jax implementation of the GPT-3 architecture](https://github.com/kingoflolz/mesh-transformer-jax/). GPT-J refers to the class of models, while 6B represents the number of parameters of this particular pre-trained model.
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GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
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| Hyperparameter | Value |
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|-------------------|--------|
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| n_parameters | 6,053,381,344 |
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| n_layers | 28* |
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| d_model | 4,096 |
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| d_ff | 16,384 |
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| n_heads | 16 |
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| d_head | 256 |
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| n_ctx | 2,048 |
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| n_vocab | 50,257 (same tokenizer as GPT-2/3) |
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| position encoding | [Rotary position encodings (RoPE)](https://arxiv.org/abs/2104.09864) |
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| RoPE dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
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<figure>
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`*` each layer consists of one feedforward block and one self attention block
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| Hyperparameter | Value |
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|----------------------|---------------|
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| \\(n_{parameters}\\) | 6,053,381,344 |
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| \\(n_{layers}\\) | 28* |
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| \\(d_{model}\\) | 4,096 |
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| \\(d_{ff}\\) | 16,384 |
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| \\(n_{heads}\\) | 16 |
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| \\(d_{head}\\) | 256 |
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| \\(n_{ctx}\\) | 2,048 |
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| \\(n_{vocab}\\) | 50,257 (same tokenizer as GPT-2/3) |
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| Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) |
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| RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
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<figcaption><strong>*</strong> Each layer consists of one feedforward block and one self attention block.</figcaption></figure>
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The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
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dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64
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dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64
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dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
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GPT-2/GPT-3.
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## Training data
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GPT-J 6B was trained on the [Pile](pile.eleuther.ai), a large scale curated dataset created by EleutherAI for the purpose of training this model.
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GPT-J 6B was trained on [the Pile](https://pile.eleuther.ai), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai).
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## Training procedure
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This model was trained for [number] billion tokens over [number] steps. It was trained as a masked autoregressive language model, using cross-entropy loss.
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This model was trained for 402 billion tokens over 383,500 steps on TPU v3-256 pod. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly.
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## Intended Use and Limitations
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This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
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GPT-J learns an inner representation of the English language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt.
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### How to use
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You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
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```py
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```python
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>>> from transformers import pipeline
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>>> generator = pipeline('text-generation', model='EleutherAI/gpt-j-6B')
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>>> generator("EleutherAI has", do_sample=True, min_length=50)
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### Limitations and Biases
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GPT-J was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J with er that the statistically most likely next token is often not the same thing as the most accurate,
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The core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon GPT-J to produce factually accurate output.
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GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
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GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See [Sections 5 and 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a more detailed analysis of the biases in the Pile.
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As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
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As with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
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## Eval results
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## Evaluation results
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Models roughly sorted by performance, or by FLOPs if not available.
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<figure>
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| Model | Public | Training FLOPs | LAMBADA PPL ↓ | LAMBADA Acc ↑ | Winogrande ↑ | Hellaswag ↑ | PIQA ↑ | Dataset Size (GB) |
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|-----------------|---------|----------------|--- |--- |--- |--- |--- |-------------------|
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| Chance | ✔ | 0 | ~a lot | ~0% | 50% | 25% | 25% | 0 |
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| GPT-3-Ada‡ | ✘ | ----- | 9.95 | 51.6% | 52.9% | 43.4% | 70.5% | ----- |
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| GPT-2-1.5B | ✔ | ----- | 10.63 | 51.21% | 59.4% | 50.9% | 70.8% | 40 |
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| GPTNeo-1.3B‡ | ✔ | 3.0e21 | 7.50 | 57.2% | 55.0% | 48.9% | 71.1% | 825 |
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| Megatron-2.5B* | ✘ | 2.4e21 | ----- | 61.7% | ----- | ----- | ----- | 174 |
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| GPTNeo-2.7B‡ | ✔ | 6.8e21 | 5.63 | 62.2% | 56.5% | 55.8% | 73.0% | 825 |
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| GPT-3-1.3B*‡ | ✘ | 2.4e21 | 5.44 | 63.6% | 58.7% | 54.7% | 75.1% | ~800 |
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| GPT-3-Babbage‡ | ✘ | ----- | 5.58 | 62.4% | 59.0% | 54.5% | 75.5% | ----- |
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| Megatron-8.3B* | ✘ | 7.8e21 | ----- | 66.5% | ----- | ----- | ----- | 174 |
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| GPT-3-2.7B*‡ | ✘ | 4.8e21 | 4.60 | 67.1% | 62.3% | 62.8% | 75.6% | ~800 |
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| Megatron-11B† | ✔ | 1.0e22 | ----- | ----- | ----- | ----- | ----- | 161 |
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| **GPT-J-6B**‡ | ✔ | **1.5e22** | **3.99** | **69.7%** | **65.3%** | **66.1%** | **76.5%** | **825** |
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| GPT-3-6.7B*‡ | ✘ | 1.2e22 | 4.00 | 70.3% | 64.5% | 67.4% | 78.0% | ~800 |
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| GPT-3-Curie‡ | ✘ | ----- | 4.00 | 69.3% | 65.6% | 68.5% | 77.9% | ----- |
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| GPT-3-13B*‡ | ✘ | 2.3e22 | 3.56 | 72.5% | 67.9% | 70.9% | 78.5% | ~800 |
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| GPT-3-175B*‡ | ✘ | 3.1e23 | 3.00 | 76.2% | 70.2% | 78.9% | 81.0% | ~800 |
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| GPT-3-Davinci‡ | ✘ | ----- | 3.0 | 75% | 72% | 78% | 80% | ----- |
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| Model | Public | Training FLOPs | LAMBADA PPL ↓ | LAMBADA Acc ↑ | Winogrande ↑ | Hellaswag ↑ | PIQA ↑ | Dataset Size (GB) |
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|--------------------------|-------------|----------------|--- |--- |--- |--- |--- |-------------------|
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| Random Chance | ✓ | 0 | ~a lot | ~0% | 50% | 25% | 25% | 0 |
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| GPT-3 Ada‡ | ✗ | ----- | 9.95 | 51.6% | 52.9% | 43.4% | 70.5% | ----- |
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| GPT-2 1.5B | ✓ | ----- | 10.63 | 51.21% | 59.4% | 50.9% | 70.8% | 40 |
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| GPT-Neo 1.3B‡ | ✓ | 3.0e21 | 7.50 | 57.2% | 55.0% | 48.9% | 71.1% | 825 |
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| Megatron-2.5B* | ✗ | 2.4e21 | ----- | 61.7% | ----- | ----- | ----- | 174 |
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| GPT-Neo 2.7B‡ | ✓ | 6.8e21 | 5.63 | 62.2% | 56.5% | 55.8% | 73.0% | 825 |
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| GPT-3 1.3B*‡ | ✗ | 2.4e21 | 5.44 | 63.6% | 58.7% | 54.7% | 75.1% | ~800 |
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| GPT-3 Babbage‡ | ✗ | ----- | 5.58 | 62.4% | 59.0% | 54.5% | 75.5% | ----- |
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| Megatron-8.3B* | ✗ | 7.8e21 | ----- | 66.5% | ----- | ----- | ----- | 174 |
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| GPT-3 2.7B*‡ | ✗ | 4.8e21 | 4.60 | 67.1% | 62.3% | 62.8% | 75.6% | ~800 |
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| Megatron-11B† | ✓ | 1.0e22 | ----- | ----- | ----- | ----- | ----- | 161 |
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| **GPT-J 6B‡** | **✓** | **1.5e22** | **3.99** | **69.7%** | **65.3%** | **66.1%** | **76.5%** | **825** |
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| GPT-3 6.7B*‡ | ✗ | 1.2e22 | 4.00 | 70.3% | 64.5% | 67.4% | 78.0% | ~800 |
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| GPT-3 Curie‡ | ✗ | ----- | 4.00 | 69.3% | 65.6% | 68.5% | 77.9% | ----- |
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| GPT-3 13B*‡ | ✗ | 2.3e22 | 3.56 | 72.5% | 67.9% | 70.9% | 78.5% | ~800 |
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| GPT-3 175B*‡ | ✗ | 3.1e23 | 3.00 | 76.2% | 70.2% | 78.9% | 81.0% | ~800 |
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| GPT-3 Davinci‡ | ✗ | ----- | 3.0 | 75% | 72% | 78% | 80% | ----- |
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<figcaption><p>Models roughly sorted by performance, or by FLOPs if not available.</p>
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`*` represents evaluation numbers reported by their respective authors, all other numbers are provided by
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running the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/) either with the released
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<p><strong>*</strong> Evaluation numbers reported by their respective authors. All other numbers are provided by
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running <a href="https://github.com/EleutherAI/lm-evaluation-harness/"><code>lm-evaluation-harness</code></a> either with released
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weights or with API access. Due to subtle implementation differences as well as different zero shot task framing, these
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might not be directly comparable. See [this blog post](https://www.eleuther.ai/research-log/gpt3-model-sizes/) for more
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details.
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might not be directly comparable. See <a href="https://blog.eleuther.ai/gpt3-model-sizes/">this blog post</a> for more
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details.</p>
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`†` The Megatron-11B model provides no comparable metrics, and several implementations using the released weights do not
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reproduce the generation quality and evaluations. (see [1](https://github.com/huggingface/transformers/pull/10301)
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[2](https://github.com/pytorch/fairseq/issues/2358) [3](https://github.com/pytorch/fairseq/issues/2719))
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Thus, evaluation was not attempted.
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<p><strong>†</strong> Megatron-11B provides no comparable metrics, and several implementations using the released weights do not
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reproduce the generation quality and evaluations. (see <a href="https://github.com/huggingface/transformers/pull/10301">1</a>
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<a href="https://github.com/pytorch/fairseq/issues/2358">2</a> <a href="https://github.com/pytorch/fairseq/issues/2719">3</a>)
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Thus, evaluation was not attempted.</p>
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`‡` These models have been trained with data which contains possible test set contamination. The OpenAI GPT-3 models
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<p><strong>‡</strong> These models have been trained with data which contains possible test set contamination. The OpenAI GPT-3 models
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failed to deduplicate training data for certain test sets, while the GPT-Neo models as well as this one is
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trained on The Pile, which has not been deduplicated against any test sets.
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trained on the Pile, which has not been deduplicated against any test sets.</p></figcaption></figure>
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### Down-Stream Applications
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TBD
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## Citation and Related Info
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## Citation and Related Information
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### BibTeX entry
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To cite this model:
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```
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```bibtex
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@misc{gpt-j,
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author = {Wang, Ben and Komatsuzaki, Aran},
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title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
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@ -125,9 +124,8 @@ To cite this model:
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}
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```
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To cite the codebase that trained this model:
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```
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```bibtex
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@misc{mesh-transformer-jax,
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author = {Wang, Ben},
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title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}},
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@ -137,21 +135,17 @@ To cite the codebase that trained this model:
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}
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```
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If you use this repository or any of the pretrained weights to do something cool, we would love to hear about it! Reach out on the [GitHub repo](https://github.com/kingoflolz/mesh-transformer-jax), Discord, or shoot Ben an email.
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If you use this model, we would love to hear about it! Reach out on [GitHub](https://github.com/kingoflolz/mesh-transformer-jax), Discord, or shoot Ben an email.
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## Acknowledgements
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This project would not have been possible without compute generously provided by the
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[TPU Research Cloud](https://sites.research.google/trc/) and [EleutherAI](https://eleuther.ai/).
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Thanks to the Cloud TPU team at Google for providing early access to the Cloud TPU VM alpha
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([now publicly available!](https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms))
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This project would not have been possible without compute generously provided by Google through the
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[TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms) Alpha.
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Thanks to everyone who have helped out one way or another (listed alphabetically):
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- [James Bradbury](https://twitter.com/jekbradbury) for valuable assistance with debugging JAX issues.
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- [Stella Biderman](https://www.stellabiderman.com), Kurumuz, and [Finetune](https://github.com/finetuneanon/) for converting the model to be compatible with the `transformers` package.
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- [Stella Biderman](www.stellabiderman.com), [Kurumuz](https://github.com/kurumuz/), and [Finetune](https://github.com/finetuneanon/) for converting the model to be compatible with the `transformers` package.
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- [Leo Gao](https://twitter.com/nabla_theta) for running zero shot evaluations for the baseline models for the table.
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- [Laurence Golding](https://github.com/researcher2) for adding some features to the web demo.
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- [Laurence Golding](https://github.com/researcher2/) for adding some features to the web demo.
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- [Aran Komatsuzaki](https://twitter.com/arankomatsuzaki) for advice with experiment design and writing the blog posts.
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- [Janko Prester](https://github.com/jprester) for creating the web demo frontend.
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- [Janko Prester](https://github.com/jprester/) for creating the web demo frontend.
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