https://github.com/hayotensor/subnet-llm

https://github.com/hayotensor/subnet-llm

Repository

https://github.com/hayotensor/subnet-llm/blob/main/

    Petals Tensor


    Visit our website!


    Subnet 1 - the first installment of Hypertensor subnets.

    Run large language models at home, BitTorrent-style.

    Fine-tuning and inference up to 10x faster than offloading
    


    



Generate text with distributed **Llama 2** (70B), **Falcon** (40B+), **BLOOM** (176B) (or their derivatives), and finetune them for your own tasks — right from your desktop computer or Google Colab:

```python
from transformers import AutoTokenizer
from petals import AutoDistributedModelForCausalLM

# Choose any model available at https://health.petals.dev
model_name = "petals-team/StableBeluga2"  # This one is fine-tuned Llama 2 (70B)

# Connect to a distributed network hosting model layers
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoDistributedModelForCausalLM.from_pretrained(model_name)

# Run the model as if it were on your computer
inputs = tokenizer("A cat sat", return_tensors="pt")["input_ids"]
outputs = model.generate(inputs, max_new_tokens=5)
print(tokenizer.decode(outputs[0]))  # A cat sat on a mat...
```


      Try now in Colab


 **Privacy.** Your data will be processed with the help of other people in the public swarm. Learn more about privacy [here](https://github.com/bigscience-workshop/petals/wiki/Security,-privacy,-and-AI-safety). For sensitive data, you can set up a [private swarm](https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm) among people you trust.

 **Want to run Llama 2?** Request access to its weights at the  [Meta AI website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and  [Model Hub](https://huggingface.co/meta-llama/Llama-2-70b-hf), then run `huggingface-cli login` in the terminal before loading the model. Or just try it in our [chatbot app](https://chat.petals.dev).

 **Any questions?** Ping us in [our Discord](https://discord.gg/KdThf2bWVU)!

## Getting Started

**Install**:

```cd``` into directory, start your virtual environment and install the repository:

```bash
python -m venv .venv
source .venv/bin/activate
python -m pip install .
```

**Update .env**:

Copy the ```.env.example``` file, rename it to ```.env``` in the root directory, and insert your seed phrase:

```bash
PHRASE=""
```

**Update RPC**:

In ```.env```, update the ```DEV_URL``` with a live RPC IP and port:

If the RPC isn't correct you will likely receive a ```ConnectionRefusedError: [Errno 111] Connection refused``` error.

```bash
DEV_URL = "ws://127.000.000.000:9945"
```

**Run Server**:

Before running your server, ensure your account has enough balance for the required minimum stake.

Use the port you have open specifically for Petals Tensor for ```--port```, and use the port the blockchain will call for testing your peer for ```--tcp_port```.

```bash
python -m petals_tensor.cli.run_server [model_path] --public_ip [public_ip] --port [port] --tcp_public_ip [tcp_public_ip] --tcp_port [tcp_port]
```

**Arguments**:

--`model_path`: The HuggingFace model path.

--`public_ip`: The public IP of the server for other peers to connect to.

--`port`: The port of the server for other peers to connect to.

--`tcp_public_ip`: The IP for the blockchain to call.

--`tcp_port`: The port for the blockchain to call.



**Socials**:

Message us!

 Discord: [our Discord](https://discord.gg/bY7NUEweQp)!

 Twitter: [our Twitter](https://twitter.com/hyper_tensor)!

## The following is the original Petals documentation. Much of this will still apply but refer to the documentation here: 

## Connect your GPU and increase Petals capacity

Petals is a community-run system — we rely on people sharing their GPUs. You can check out [available models](https://health.petals.dev) and help serving one of them! As an example, here is how to host a part of [Stable Beluga 2](https://huggingface.co/stabilityai/StableBeluga2) on your GPU:

 **Linux + Anaconda.** Run these commands for NVIDIA GPUs (or follow [this](https://github.com/bigscience-workshop/petals/wiki/Running-on-AMD-GPU) for AMD):

```bash
conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
pip install git+https://github.com/bigscience-workshop/petals
python -m petals.cli.run_server petals-team/StableBeluga2
```

 **Windows + WSL.** Follow [this guide](https://github.com/bigscience-workshop/petals/wiki/Run-Petals-server-on-Windows) on our Wiki.

 **Docker.** Run our [Docker](https://www.docker.com) image for NVIDIA GPUs (or follow [this](https://github.com/bigscience-workshop/petals/wiki/Running-on-AMD-GPU) for AMD):

```bash
sudo docker run -p 31330:31330 --ipc host --gpus all --volume petals-cache:/cache --rm \
    learningathome/petals:main \
    python -m petals.cli.run_server --port 31330 petals-team/StableBeluga2
```

 **macOS + Apple M1/M2 GPU.** Install [Homebrew](https://brew.sh/), then run these commands:

```bash
brew install python
python3 -m pip install git+https://github.com/bigscience-workshop/petals
python3 -m petals.cli.run_server petals-team/StableBeluga2
```


      Learn more (how to use multiple GPUs, start the server on boot, etc.)


 **Any questions?** Ping us in [our Discord](https://discord.gg/X7DgtxgMhc)!

 **Want to host Llama 2?** Request access to its weights at the  [Meta AI website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and  [Model Hub](https://huggingface.co/meta-llama/Llama-2-70b-hf), generate an  [access token](https://huggingface.co/settings/tokens), then add `--token YOUR_TOKEN_HERE` to the `python -m petals.cli.run_server` command.

 **Security.** Hosting a server does not allow others to run custom code on your computer. Learn more [here](https://github.com/bigscience-workshop/petals/wiki/Security,-privacy,-and-AI-safety).

 **Thank you!** Once you load and host 10+ blocks, we can show your name or link on the [swarm monitor](https://health.petals.dev) as a way to say thanks. You can specify them with `--public_name YOUR_NAME`.

## How does it work?

- You load a small part of the model, then join a [network](https://health.petals.dev) of people serving the other parts. Singlebatch inference runs at up to **6 tokens/sec** for **Llama 2** (70B) and up to **4 tokens/sec** for **Falcon** (180B)  enough for [chatbots](https://chat.petals.dev) and interactive apps.
- You can employ any fine-tuning and sampling methods, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of **PyTorch** and ** Transformers**.


    



      Read paper
              
      See FAQ


##  Tutorials, examples, and more

Basic tutorials:

- Getting started: [tutorial](https://colab.research.google.com/drive/1uCphNY7gfAUkdDrTx21dZZwCOUDCMPw8?usp=sharing)
- Prompt-tune Llama-65B for text semantic classification: [tutorial](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-sst2.ipynb)
- Prompt-tune BLOOM to create a personified chatbot: [tutorial](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-personachat.ipynb)

Useful tools:

- [Chatbot web app](https://chat.petals.dev) (connects to Petals via an HTTP/WebSocket endpoint): [source code](https://github.com/petals-infra/chat.petals.dev)
- [Monitor](https://health.petals.dev) for the public swarm: [source code](https://github.com/petals-infra/health.petals.dev)

Advanced guides:

- Launch a private swarm: [guide](https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm)
- Run a custom model: [guide](https://github.com/bigscience-workshop/petals/wiki/Run-a-custom-model-with-Petals)

### Benchmarks

Please see **Section 3.3** of our [paper](https://arxiv.org/pdf/2209.01188.pdf).

###  Contributing

Please see our [FAQ](https://github.com/bigscience-workshop/petals/wiki/FAQ:-Frequently-asked-questions#contributing) on contributing.

###  Citation

Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Max Ryabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, and Colin Raffel.
[Petals: Collaborative Inference and Fine-tuning of Large Models.](https://arxiv.org/abs/2209.01188)
_arXiv preprint arXiv:2209.01188,_ 2022.

```bibtex
@article{borzunov2022petals,
  title = {Petals: Collaborative Inference and Fine-tuning of Large Models},
  author = {Borzunov, Alexander and Baranchuk, Dmitry and Dettmers, Tim and Ryabinin, Max and Belkada, Younes and Chumachenko, Artem and Samygin, Pavel and Raffel, Colin},
  journal = {arXiv preprint arXiv:2209.01188},
  year = {2022},
  url = {https://arxiv.org/abs/2209.01188}
}
```

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


    This project is a part of the BigScience research workshop.


    

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