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---
language:
- en
tags:
- llm-rs
- ggml
pipeline_tag: text-generation
datasets:
- the_pile
---
# GGML converted versions of [EleutherAI](https://huggingface.co/EleutherAI)'s GPT-J 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* |
| \\(d_{model}\\) | 4096 |
| \\(d_{ff}\\) | 16384 |
| \\(n_{heads}\\) | 16 |
| \\(d_{head}\\) | 256 |
| \\(n_{ctx}\\) | 2048 |
| \\(n_{vocab}\\) | 50257/50400† (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>*</strong> Each layer consists of one feedforward block and one self attention block.</p>
<p><strong>†</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.
## Converted Models
| Name | Based on | Type | Container | GGML Version |
|:---------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:-------|:------------|:---------------|
| [gpt-j-6b-f16.bin](https://huggingface.co/rustformers/gpt-j-ggml/blob/main/gpt-j-6b-f16.bin) | [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) | F16 | GGML | V3 |
| [gpt-j-6b-q4_0.bin](https://huggingface.co/rustformers/gpt-j-ggml/blob/main/gpt-j-6b-q4_0.bin) | [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) | Q4_0 | GGML | V3 |
| [gpt-j-6b-q4_0-ggjt.bin](https://huggingface.co/rustformers/gpt-j-ggml/blob/main/gpt-j-6b-q4_0-ggjt.bin) | [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) | Q4_0 | GGJT | V3 |
| [gpt-j-6b-q5_1.bin](https://huggingface.co/rustformers/gpt-j-ggml/blob/main/gpt-j-6b-q5_1.bin) | [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) | Q5_1 | GGML | V3 |
| [gpt-j-6b-q5_1-ggjt.bin](https://huggingface.co/rustformers/gpt-j-ggml/blob/main/gpt-j-6b-q5_1-ggjt.bin) | [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) | Q5_1 | GGJT | V3 |
## Usage
### Python via [llm-rs](https://github.com/LLukas22/llm-rs-python):
#### Installation
Via pip: `pip install llm-rs`
#### Run inference
```python
from llm_rs import AutoModel
#Load the model, define any model you like from the list above as the `model_file`
model = AutoModel.from_pretrained("rustformers/gpt-j-ggml",model_file="gpt-j-6b-q4_0-ggjt.bin")
#Generate
print(model.generate("The meaning of life is"))
```
### Rust via [Rustformers/llm](https://github.com/rustformers/llm):
#### Installation
```
git clone --recurse-submodules https://github.com/rustformers/llm.git
cd llm
cargo build --release
```
#### Run inference
```
cargo run --release -- gptj infer -m path/to/model.bin -p "Tell me how cool the Rust programming language is:"
``` |