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https://github.com/petals-infra/chat.petals.dev
💬 Chatbot web app + HTTP and Websocket endpoints for LLM inference with the Petals client
https://github.com/petals-infra/chat.petals.dev
api bloom chatbot distributed-systems gpt guanaco language-models large-language-models llama llama2 transformer volunteer-computing
Last synced: about 1 month ago
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💬 Chatbot web app + HTTP and Websocket endpoints for LLM inference with the Petals client
- Host: GitHub
- URL: https://github.com/petals-infra/chat.petals.dev
- Owner: petals-infra
- Created: 2022-12-28T01:11:15.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-22T19:16:06.000Z (7 months ago)
- Last Synced: 2024-10-10T06:09:30.045Z (about 1 month ago)
- Topics: api, bloom, chatbot, distributed-systems, gpt, guanaco, language-models, large-language-models, llama, llama2, transformer, volunteer-computing
- Language: Python
- Homepage: https://chat.petals.dev
- Size: 232 KB
- Stars: 306
- Watchers: 11
- Forks: 79
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
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README
# Petals Chat
A chatbot [web app](https://chat.petals.dev) + HTTP and WebSocket endpoints for LLM inference with the [Petals](https://petals.dev) client
## Interactive Chat
You can try it out at **https://chat.petals.dev** or run the backend on your server using these commands:
```bash
git clone https://github.com/petals-infra/chat.petals.dev.git
cd chat.petals.dev
pip install -r requirements.txt
flask run --host=0.0.0.0 --port=5000
```
🦙 **Want to serve 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 starting the web app. If you don't want Llama 2, just remove the `meta-llama` models from [config.py](https://github.com/petals-infra/chat.petals.dev/blob/main/config.py).
🦄 **Deploying with Gunicorn.** In production, we recommend using gunicorn instead of the Flask dev server:
```bash
gunicorn app:app --bind 0.0.0.0:5000 --worker-class gthread --threads 100 --timeout 1000
```
The chat uses the WebSocket API under the hood.
## APIs
The backend provides two APIs endpoints:
- [WebSocket API](#websocket-api-apiv2generate) (`/api/v2/generate`, recommended)
- [HTTP API](#http-api-apiv1) (`/api/v1/...`)
Please use the WebSocket API when possible - it is much faster, more powerful, and consumes less resources.
If you develop your own web app, you can use our endpoint at `https://chat.petals.dev/api/...` for research and development, then set up your own backend for production using the commands above.
> **Note:** We do not recommend using the endpoint at `https://chat.petals.dev/api/...` in production. It has a limited throughput, and we may pause or stop it any time.
Endpoint's system requirements
- If you use a CPU-only server, you need enough RAM to fit embeddings for all models (see the table below).
If your CPU supports AVX512, the embeddings will be loaded in 16-bit, otherwise they will be loaded in 32-bit (= 2x more memory).
This is because multiplying 16-bit weights without AVX512 is slow and may introduce a slowdown of 1-2 sec/token.
AVX512 support is available on late Intel Xeon CPUs
(e.g., on [DigitalOcean](https://digitalocean.com) droplets with a dedicated CPU).
- If you use a GPU server, you need enough GPU memory to fit the embeddings for all models.
The embeddings will be loaded in 16-bit.
- You don't have to serve all models. If you don't have enough memory, remove some models in [config.py](config.py).
| Model family | Embeds in 16-bit | Embeds in 32-bit |
| --- | --- | --- |
| Llama 2 (70B, 70B-Chat), Llama-65B, Guanaco-65B | 1.05 GB | 2.1 GB |
| BLOOM-176B, BLOOMZ-176B | 7.19 GB | 14.38 GB |
## WebSocket API (`/api/v2/generate`)
This API implies that you open a WebSocket connection and exchange JSON-encoded requests and responses.
This may be done from any programming language.
Example code (Javascript)
This code opens an inference session with the [stabilityai/StableBeluga2](https://huggingface.co/stabilityai/StableBeluga2) model, sends the prompt "A cat sat on",
and samples new tokens until the total length reaches 30 tokens. Sampling is done with [temperature](https://huggingface.co/blog/how-to-generate#sampling) = 0.6 and [top_p](https://huggingface.co/blog/how-to-generate#top-p-nucleus-sampling) = 0.9.
```javascript
const ws = new WebSocket(`wss://chat.petals.dev/api/v2/generate`);
ws.onopen = () => {
const prompt = "A cat sat on";
const maxLength = 30;
ws.send(JSON.stringify({
type: "open_inference_session", model: "stabilityai/StableBeluga2", max_length: maxLength
}));
ws.send(JSON.stringify({
type: "generate", inputs: prompt, max_length: maxLength, do_sample: 1, temperature: 0.6, top_p: 0.9
}));
ws.onmessage = event => {
const response = JSON.parse(event.data);
if (response.ok) {
if (response.outputs === undefined) {
console.log("Session opened, generating...");
} else {
console.log("Generated: " + prompt + response.outputs);
ws.close();
}
} else {
console.log("Error: " + response.traceback);
ws.close();
}
};
};
```
🐍 **Using Python on Linux/macOS?** Please consider running the [native Petals client](https://github.com/bigscience-workshop/petals#readme) instead.
This way, you can connect to the swarm directly (without this API endpoint) and even run fine-tuning.
The requests must follow this protocol:
### open_inference_session
The first request must be of type **open_inference_session** and include these parameters:
- **model** (str) - Model repository for one of the models defined in [config.py](https://github.com/petals-infra/chat.petals.dev/blob/main/config.py).
If you load a model with an adapter, use the adapter repository here instead.
- **max_length** (int) - Max length of generated text (including prefix and intermediate inputs) in tokens.
Notes:
- The inference session created by this request is unique to this WebSocket connection and cannot be reused in other connections.
- The session is closed automatically when the connection is closed (gracefully or abruptly).
- We do not provide API for Falcon-180B due to its [license](https://huggingface.co/spaces/tiiuae/falcon-180b-license/blob/main/LICENSE.txt) restrictions.
Request:
```javascript
{type: "open_inference_session", max_length: 1024}
```
Response:
```javascript
{ok: true} // If successful
{ok: false, traceback: "..."} // If failed
```
### generate
The next requests must be of type **generate** and include the same parameters as in the [/api/v1/generate HTTP API](#post-apiv1generate).
In contrast to HTTP API, you can use this API in streaming fashion, generating a response token-by-token and accepting intermediate prompts from a user
(e.g., to make a chatbot).
A new feature of the WebSocket API is the `stop_sequence` parameter (str, optional). If you set it, the server will continue generation with the same parameters unless it generates the `stop_sequence`, so you may get multiple responses without having to send the request again and wait for the round trip's latency.
Intermediate responses contain the field `stop: false`, and the last response contains `stop: true`. For example, you can set `max_new_tokens: 1` and receive tokens one by one, as soon as they are generated. Check out the chat's [frontend code](static/chat.js) for a detailed example of how to do that.
Request:
```javascript
{type: "generate", "inputs": "A cat in French is \"", "max_new_tokens": 3}
```
Response (one or multiple):
```javascript
{ok: true, outputs: "chat\".", stop: true} // If successful
{ok: false, traceback: "..."} // If failed
```
## HTTP API (`/api/v1/...`)
### POST /api/v1/generate
Parameters:
- **model** (str) - Model repository for one of the models defined in [config.py](https://github.com/petals-infra/chat.petals.dev/blob/main/config.py).
If you load a model with an adapter, use the adapter repository here instead.
- **inputs** (str, optional) - New user inputs. May be omitted if you continue generation in an inference session (see below).
- **max_length** (int) - Max length of generated text (including prefix) in tokens.
- **max_new_tokens** (int) - Max number of newly generated tokens (excluding prefix).
Generation parameters (compatible with [.generate()](https://huggingface.co/blog/how-to-generate) from 🤗 Transformers):
- **do_sample** (bool, optional) - If `0` (default), runs [greedy generation](https://huggingface.co/blog/how-to-generate#greedy-search).
If `1`, performs [sampling](https://huggingface.co/blog/how-to-generate#sampling) with parameters below.
- **temperature** (float, optional) - Temperature for sampling.
- **top_k** (int, optional) - [Top-k](https://huggingface.co/blog/how-to-generate#top-k-sampling) sampling.
- **top_p** (float, optional) - [Top-p](https://huggingface.co/blog/how-to-generate#top-p-nucleus-sampling) (nucleus) sampling.
- **repetition_penalty** (float, optional) - [Repetition penalty](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationConfig.repetition_penalty), see [paper](https://arxiv.org/abs/1909.05858).
Notes:
- You need to specify either `max_length` or `max_new_tokens`.
- If you'd like to solve downstream tasks in the zero-shot mode, start with `do_sample=0` (default).
- If you'd like to make a chat bot or write a long text, start with `do_sample=1, temperature=0.6, top_p=0.9`.
- We do not provide API for Falcon-180B due to its [license](https://huggingface.co/spaces/tiiuae/falcon-180b-license/blob/main/LICENSE.txt) restrictions.
Returns (JSON):
- **ok** (bool)
- **outputs** (str)
- **traceback** (str) - the Python traceback if `ok == False`
Example (curl):
```bash
$ curl -X POST "https://chat.petals.dev/api/v1/generate" -d "model=meta-llama/Llama-2-70b-chat-hf" -d "inputs=Once upon a time," -d "max_new_tokens=20"
{"ok":true,"outputs":" there was a young woman named Sophia who lived in a small village nestled in the rolling hills"}
```