# Chat with Meta's LLaMA models at home made easy

This repository is a chat example with [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) ([arXiv](https://arxiv.org/abs/2302.13971v1)) models running on a typical home PC. You will just need a NVIDIA videocard and some RAM to chat with model.

This repo is heavily based on Meta's original repo: https://github.com/facebookresearch/llama

And on Steve Manuatu's repo: https://github.com/venuatu/llama

And on Shawn Presser's repo: https://github.com/shawwn/llama

HF 🤗 version by Yam Peleg and Jason Phang: https://github.com/ypeleg/llama & https://github.com/zphang

### Examples of chats here

https://github.com/facebookresearch/llama/issues/162

Share your best prompts, chats or generations here in this issue: https://github.com/randaller/llama-chat/issues/7

### System requirements
- Modern enough CPU
- NVIDIA graphics card (2 Gb of VRAM is ok)
- 64 or better 128 Gb of RAM (192 would be perfect for 65B model)

One may run with 32 Gb of RAM, but inference will be slow (with the speed of your swap file reading)

I am running this on a [12700k/128 Gb RAM/NVIDIA 3070ti 8Gb/fast huge nvme with 256 Gb swap for 65B model] and getting one token from 30B model in a few seconds.

For example, **30B model uses around 70 Gb of RAM**. 7B model fits into 18 Gb. 13B model uses 48 Gb.

If you do not have nvidia videocard, you may use another repo for cpu-only inference: https://github.com/randaller/llama-cpu

### Conda Environment Setup Example for Windows 10+
Download and install Anaconda Python https://www.anaconda.com and run Anaconda Prompt
```
conda create -n llama python=3.10
conda activate llama
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
```

### Setup
In a conda env with pytorch / cuda available, run
```
pip install -r requirements.txt
```
Then in this repository
```
pip install -e .
```

### Download tokenizer and models
magnet:?xt=urn:btih:ZXXDAUWYLRUXXBHUYEMS6Q5CE5WA3LVA&dn=LLaMA

or

magnet:xt=urn:btih:b8287ebfa04f879b048d4d4404108cf3e8014352&dn=LLaMA&tr=udp%3a%2f%2ftracker.opentrackr.org%3a1337%2fannounce

### Prepare model

First, you need to unshard model checkpoints to a single file. Let's do this for 30B model.

```
python merge-weights.py --input_dir D:\Downloads\LLaMA --model_size 30B
```

In this example, D:\Downloads\LLaMA is a root folder of downloaded torrent with weights.

This will create merged.pth file in the root folder of this repo.

Place this file and corresponding (torrentroot)/30B/params.json of model into [/model] folder.

So you should end up with two files in [/model] folder: merged.pth and params.json.

Place (torrentroot)/tokenizer.model file to the [/tokenizer] folder of this repo. Now you are ready to go.

### Run the chat

```
python example-chat.py ./model ./tokenizer/tokenizer.model
```

### Generation parameters

![image](https://user-images.githubusercontent.com/22396871/224481306-0079dc71-a659-46f2-96a3-38d8a0b8bafc.png)

**Temperature** is one of the key parameters of generation. You may wish to play with temperature. The more temperature is, the model will use more "creativity", and the less temperature instruct model to be "less creative", but following your prompt stronger.

**Repetition penalty** is a feature implemented by Shawn Presser. With this, the model will be fined, when it would like to enter to repetion loop state. Set this parameter to 1.0, if you wish to disable this feature.

**Samplers**

By default, Meta provided us with top_p sampler only. Again, Shawn added an alternate top_k sampler, which (in my tests) performs pretty well. If you wish to switch to top_k sampler, use the following parameters:

```
temperature: float = 0.7,
top_p: float = 0.0,
top_k: int = 40,
sampler: str = 'top_k',
```

For sure, you may play with all the values to get different outputs.

**Launch examples**

One may modify these hyperparameters straight in the code. But it is better to leave the defaults in code and set the parameters of experiments in the launch line.

```
# Run with top_p sampler, with temperature 0.75, with top_p value 0.95, repetition penalty disabled
python example-chat.py ./model ./tokenizer/tokenizer.model 0.75 0.95 0 1.0 top_p

# Run with top_k sampler, with temperature 0.7, with top_k value 40, default repetition penalty value
python example-chat.py ./model ./tokenizer/tokenizer.model 0.7 0.0 40 1.17 top_k
```

Of course, this is also applicable to a [python example.py] as well (see below).


### Enable multi-line answers

If you wish to stop generation not by "\n" sign, but by another signature, like "User:" (which is also good idea), or any other, make the following modification in the llama/generation.py:

![image](https://user-images.githubusercontent.com/22396871/224122767-227deda4-a718-4774-a7f9-786c07d379cf.png)

-5 means to remove last 5 chars from resulting context, which is length of your stop signature, "User:" in this example.

### Share the best with community

Share your best prompts and generations with others here: https://github.com/randaller/llama-chat/issues/7

### Typical generation with prompt (not a chat)

Simply comment three lines in llama/generation.py to turn it to a generator back.

![image](https://user-images.githubusercontent.com/22396871/224283389-e29de04e-28d1-4ccd-bf6b-81b29828d3eb.png)

```
python example.py ./model ./tokenizer/tokenizer.model
```

Confirming that 30B model is able to generate code and fix errors in code: https://github.com/randaller/llama-chat/issues/7

Confirming that 30B model is able to generate prompts for Stable Diffusion: https://github.com/randaller/llama-chat/issues/7#issuecomment-1463691554

Confirming that 7B and 30B model support Arduino IDE: https://github.com/randaller/llama-chat/issues/7#issuecomment-1464179944

Confirming that 30B model is able to generate SQL code: https://github.com/randaller/llama-chat/issues/7#issuecomment-1467861922

### Hugging Face 🤗 version

Thanks to Yam Peleg, we now have *"No overengineering bullshit"* version. Model shards and tokenizer will be downloaded from HF automatically at the first run. They will be cached in [C:\Users\USERNAME\\.cache\huggingface\hub] folder under Windows, so do not forget to clean up to 250 Gb after experiments.

```
python hf-inference-example.py
```

### Reference

LLaMA: Open and Efficient Foundation Language Models -- https://arxiv.org/abs/2302.13971

```
@article{touvron2023llama,
  title={LLaMA: Open and Efficient Foundation Language Models},
  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
  journal={arXiv preprint arXiv:2302.13971},
  year={2023}
}
```