Writer
/

palmyra-small

@@ -30,94 +30,129 @@ img {
 Writer-small 128M is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while. It has Tensor Parallelism (TP) of 1, Pipeline Parallelism (PP) of 1 and should fit on a single NVIDIA GPU.
-## Getting started
-### Step 1: Install Writer-small and dependencies
-You will need to install NVIDIA Apex.
-```
-git clone https://github.com/ericharper/apex.git
-cd apex
-git checkout nm_v1.11.0
-pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" --global-option="--distributed_adam" --global-option="--deprecated_fused_adam" ./
-```
-```
-pip install nemo_toolkit['nlp']==1.11.0
-```
-### Step 2: Launch eval server
-**Note.** The model has been trained with Tensor Parallelism (TP) of 1 and Pipeline Parallelism (PP) of 1 and should fit on a single NVIDIA GPU.
-```
-git clone https://github.com/NVIDIA/NeMo.git
-cd NeMo/examples/nlp/language_modeling
-git checkout v1.11.0
-python megatron_gpt_eval.py gpt_model_file=Writer-gpt-small.nemo server=True tensor_model_parallel_size=1 trainer.devices=1
-```
-### Step 3: Send prompts to your model!
-```python
-import json
-import requests
-port_num = 5555
-headers = {"Content-Type": "application/json"}
-def request_data(data):
-    resp = requests.put('http://localhost:{}/generate'.format(port_num),
-                        data=json.dumps(data),
-                        headers=headers)
-    sentences = resp.json()['sentences']
-    return sentences
-data = {
-    "sentences": ["Tell me an interesting fact about space travel."]*1,
-    "tokens_to_generate": 50,
-    "temperature": 1.0,
-    "add_BOS": True,
-    "top_k": 0,
-    "top_p": 0.9,
-    "greedy": False,
-    "all_probs": False,
-    "repetition_penalty": 1.2,
-    "min_tokens_to_generate": 2,
-}
-sentences = request_data(data)
-print(sentences)
-```
-## Training Data
-The model was trained on ["The Piles" dataset prepared by Eleuther.AI](https://pile.eleuther.ai/). [4]
-## Evaluation results
-*Zero-shot performance.* Evaluated using [LM Evaluation Test Suite from AI21](https://github.com/AI21Labs/lm-evaluation)
-| ARC-Challenge	| ARC-Easy | RACE-middle | RACE-high | Winogrande | RTE | BoolQA | HellaSwag | PiQA |
-| ------------- | -------- | ----------- | --------- | ---------- | --- | ------ | --------- | ---- |
-| 0.3012        | 0.4596  | 0.459       | 0.3797    | 0.5343     | 0.5451 | 0.5979 | 0.4443 | 0.6834 |
-## Limitations
-The model was trained on the data originally crawled from the Internet. This data contains toxic language and societal biases. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.
-## References
-[1] [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
-[2] [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf)
-[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
-[4] [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027)
-## Licence
-License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.

 Writer-small 128M is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while. It has Tensor Parallelism (TP) of 1, Pipeline Parallelism (PP) of 1 and should fit on a single NVIDIA GPU.
+# GPT-J 6B
+## Model Description
+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.
+<figure>
+| Hyperparameter       | Value      |
+|----------------------|------------|
+| \\(n_{parameters}\\) | 6053381344 |
+| \\(n_{layers}\\)     | 28&ast;    |
+| \\(d_{model}\\)      | 4096       |
+| \\(d_{ff}\\)         | 16384      |
+| \\(n_{heads}\\)      | 16         |
+| \\(d_{head}\\)       | 256        |
+| \\(n_{ctx}\\)        | 2048       |
+| \\(n_{vocab}\\)      | 50257/50400&dagger; (same tokenizer as GPT-2/3)  |
+| Positional Encoding  | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) |
+| RoPE Dimensions      | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
+<figcaption><p><strong>&ast;</strong> Each layer consists of one feedforward block and one self attention block.</p>
+<p><strong>&dagger;</strong> Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer.</p></figcaption></figure>
+The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
+dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64
+dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
+GPT-2/GPT-3.
+## Training data
+GPT-J 6B was trained on [the Pile](https://pile.eleuther.ai), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai).
+## Training procedure
+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.
+## Intended Use and Limitations
+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.
+### How to use
+This model can be easily loaded using the `AutoModelForCausalLM` functionality:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
+model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
+```
+### Limitations and Biases
+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.
+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.
+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.
+## Evaluation results
+<figure>
+|  Model                   | Public      | Training FLOPs | LAMBADA PPL ↓ | LAMBADA Acc ↑ | Winogrande ↑ | Hellaswag ↑ | PIQA ↑    | Dataset Size (GB) |
+|--------------------------|-------------|----------------|---            |---            |---           |---          |---        |-------------------|
+| Random Chance            | &check;     | 0              | ~a lot        | ~0%           | 50%          | 25%         | 25%       | 0                 |
+| GPT-3 Ada&ddagger;       | &cross;     | -----          | 9.95          | 51.6%         | 52.9%        | 43.4%       | 70.5%     | -----             |
+| GPT-2 1.5B               | &check;     | -----          | 10.63         | 51.21%        | 59.4%        | 50.9%       | 70.8%     | 40                |
+| GPT-Neo 1.3B&ddagger;    | &check;     | 3.0e21         | 7.50          | 57.2%         | 55.0%        | 48.9%       | 71.1%     | 825               |
+| Megatron-2.5B&ast;       | &cross;     | 2.4e21         | -----         | 61.7%         | -----        | -----       | -----     | 174               |
+| GPT-Neo 2.7B&ddagger;    | &check;     | 6.8e21         | 5.63          | 62.2%         | 56.5%        | 55.8%       | 73.0%     | 825               |
+| GPT-3 1.3B&ast;&ddagger; | &cross;     | 2.4e21         | 5.44          | 63.6%         | 58.7%        | 54.7%       | 75.1%     | ~800              |
+| GPT-3 Babbage&ddagger;   | &cross;     | -----          | 5.58          | 62.4%         | 59.0%        | 54.5%       | 75.5%     | -----             |
+| Megatron-8.3B&ast;       | &cross;     | 7.8e21         | -----         | 66.5%         | -----        | -----       | -----     | 174               |
+| GPT-3 2.7B&ast;&ddagger; | &cross;     | 4.8e21         | 4.60          | 67.1%         | 62.3%        | 62.8%       | 75.6%     | ~800              |
+| Megatron-11B&dagger;     | &check;     | 1.0e22         | -----         | -----         | -----        | -----       | -----     | 161               |
+| **GPT-J 6B&ddagger;**    | **&check;** | **1.5e22**     | **3.99**      | **69.7%**     | **65.3%**    | **66.1%**   | **76.5%** | **825**           |
+| GPT-3 6.7B&ast;&ddagger; | &cross;     | 1.2e22         | 4.00          | 70.3%         | 64.5%        | 67.4%       | 78.0%     | ~800              |
+| GPT-3 Curie&ddagger;     | &cross;     | -----          | 4.00          | 69.3%         | 65.6%        | 68.5%       | 77.9%     | -----             |
+| GPT-3 13B&ast;&ddagger;  | &cross;     | 2.3e22         | 3.56          | 72.5%         | 67.9%        | 70.9%       | 78.5%     | ~800              |
+| GPT-3 175B&ast;&ddagger; | &cross;     | 3.1e23         | 3.00          | 76.2%         | 70.2%        | 78.9%       | 81.0%     | ~800              |
+| GPT-3 Davinci&ddagger;   | &cross;     | -----          | 3.0           | 75%           | 72%          | 78%         | 80%       | -----             |
+<figcaption><p>Models roughly sorted by performance, or by FLOPs if not available.</p>
+<p><strong>&ast;</strong> Evaluation numbers reported by their respective authors. All other numbers are provided by
+running <a href="https://github.com/EleutherAI/lm-evaluation-harness/"><code>lm-evaluation-harness</code></a> either with released
+weights or with API access. Due to subtle implementation differences as well as different zero shot task framing, these
+might not be directly comparable. See <a href="https://blog.eleuther.ai/gpt3-model-sizes/">this blog post</a> for more
+details.</p>
+<p><strong>†</strong> Megatron-11B provides no comparable metrics, and several implementations using the released weights do not
+reproduce the generation quality and evaluations. (see <a href="https://github.com/huggingface/transformers/pull/10301">1</a>
+<a href="https://github.com/pytorch/fairseq/issues/2358">2</a> <a href="https://github.com/pytorch/fairseq/issues/2719">3</a>)
+Thus, evaluation was not attempted.</p>
+<p><strong>‡</strong> These models have been trained with data which contains possible test set contamination. The OpenAI GPT-3 models
+failed to deduplicate training data for certain test sets, while the GPT-Neo models as well as this one is
+trained on the Pile, which has not been deduplicated against any test sets.</p></figcaption></figure>
+## Citation and Related Information
+### BibTeX entry
+To cite this model:
+```bibtex
+@misc{gpt-j,
+  author = {Wang, Ben and Komatsuzaki, Aran},
+  title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
+  howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
+  year = 2021,
+  month = May
+}
+```
+To cite the codebase that trained this model:
+```bibtex
+@misc{mesh-transformer-jax,
+  author = {Wang, Ben},
+  title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}},
+  howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
+  year = 2021,
+  month = May
+}
+```
+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.