---
tags:
- llm-rs
- ggml
pipeline_tag: text-generation
license: apache-2.0
language:
- en
datasets:
- togethercomputer/RedPajama-Data-1T
---
# GGML converted versions of [OpenLM Research](https://huggingface.co/openlm-research)'s LLaMA models

# OpenLLaMA: An Open Reproduction of LLaMA


In this repo, we present a permissively licensed open source reproduction of Meta AI's [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) large language model. We are releasing a 7B and 3B model trained on 1T tokens, as well as the preview of a 13B model trained on 600B tokens. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. Please see the [project homepage of OpenLLaMA](https://github.com/openlm-research/open_llama) for more details.


## Weights Release, License and Usage

We release the weights in two formats: an EasyLM format to be use with our [EasyLM framework](https://github.com/young-geng/EasyLM), and a PyTorch format to be used with the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library. Both our training framework EasyLM and the checkpoint weights are licensed permissively under the Apache 2.0 license.

## Converted Models:

$MODELS$

## 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/open-llama-ggml",model_file=" open_llama_7b-q4_0-ggjt.bin")

#Generate
print(model.generate("The meaning of life is"))
```
### Using [local.ai](https://github.com/louisgv/local.ai) GUI 

#### Installation
Download the installer at [www.localai.app](https://www.localai.app/).

#### Running Inference
Download your preferred model and place it in the "models" directory. Subsequently, you can start a chat session with your model directly from the interface.

### 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 -- llama infer -m path/to/model.bin  -p "Tell me how cool the Rust programming language is:"
```