Update README.md

README.md

@@ -1,3 +1,36 @@
 ---
+language:
+- en
+tags:
+- pytorch
+- causal-lm
 license: apache-2.0
 ---
+
+# Sparse GPT-J 6B
+
+## Model Description
+The sparse version of GPT-J 6B is a pruned variant derived from the original [GPT-J 6B](https://huggingface.co/EleutherAI/gpt-j-6b) model and the vast majority of linear layers maintain a 40% unstructured sparsity (except for the 'lm_head'). 
+
+
+<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.