
# 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&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.


## 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:"
```
{"language": ["en"], "tags": ["llm-rs", "ggml"], "datasets": ["the_pile"], "pipeline_tag": "text-generation"}
transformers llm-rs ggml text-generation en dataset:the_pile arxiv:2104.09864 endpoints_compatible region:us
TAGS
#transformers #llm-rs #ggml #text-generation #en #dataset-the_pile #arxiv-2104.09864 #endpoints_compatible #region-us 

GGML converted versions of EleutherAI's GPT-J model
===================================================


Description
-----------


GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.




**\*** Each layer consists of one feedforward block and one self attention block.


**†** Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer.


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
----------------



Usage
-----


### Python via llm-rs:


#### Installation


Via pip: 'pip install llm-rs'


#### Run inference


### Rust via Rustformers/llm:


#### Installation


#### Run inference



### Python via llm-rs: #### Installation


Via pip: 'pip install llm-rs' #### Run inference ### Rust via Rustformers/llm: #### Installation #### Run inference
TAGS
#transformers #llm-rs #ggml #text-generation #en #dataset-the_pile #arxiv-2104.09864 #endpoints_compatible #region-us 
 ### Python via llm-rs: #### Installation


Via pip: 'pip install llm-rs' #### Run inference ### Rust via Rustformers/llm: #### Installation #### Run inference