Generated README.md

README.md

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+---
+language:
+- en
+tags:
+- llm-rs
+- ggml
+pipeline_tag: text-generation
+datasets:
+- the_pile
+---
+
+# GGML covnerted Models 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-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:"
+```