From 88248bcc70504f789600a5e5f5431882bf3f8716 Mon Sep 17 00:00:00 2001 From: Hassan El Mghari Date: Sun, 23 Jun 2024 13:29:00 -0700 Subject: [PATCH 1/3] working moa script --- README.md | 49 +++++++++++++++++++++-------- assets/together-moa-explained.png | Bin 0 -> 242996 bytes moa.py | 50 ++++++++++++++++++++++++++++++ 3 files changed, 86 insertions(+), 13 deletions(-) create mode 100644 assets/together-moa-explained.png create mode 100644 moa.py diff --git a/README.md b/README.md index 48d6433..c1a362c 100644 --- a/README.md +++ b/README.md @@ -1,35 +1,59 @@ -# Mixture-of-Agents Enhances Large Language Model Capabilities +# Mixture-of-Agents (MoA) [![License](https://img.shields.io/badge/License-Apache_2.0-green.svg)](LICENSE) [![arXiv](https://img.shields.io/badge/ArXiv-2406.04692-b31b1b.svg)](https://arxiv.org/abs/2406.04692) [![Discord](https://img.shields.io/badge/Discord-Together%20AI-blue?logo=discord&logoColor=white)](https://discord.com/invite/9Rk6sSeWEG) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/togethercompute.svg?style=social&label=Follow%20%40togethercompute)](https://twitter.com/togethercompute) + + PDFToChat – Chat with your PDFs in seconds. + + +

+ Overview · + Quickstart · + Demo + · + Evaluation + · + Results + . + Credits +

+ ## Overview -
- moa -
-
+Mixture of Agents (MoA) is a novel approach that leverages the collective strengths of multiple LLMs to enhance performance, achieving state-of-the-art results. By employing a layered architecture where each layer comprises several LLM agents, **MoA significantly outperforms GPT-4 Omni’s 57.5% on AlpacaEval 2.0 with a score of 65.1%**, using only open-source models! + +## Quickstart: MoA in 50 LOC -Mixture of Agents (MoA) is a novel approach that leverages the collective strengths of multiple LLMs to enhance performance, achieving state-of-the-art results. By employing a layered architecture where each layer comprises several LLM agents, MoA significantly outperforms GPT-4 Omni’s 57.5% on AlpacaEval 2.0 with a score of 65.1%, using only open-source models! +To get to get started with using MoA in your own apps, see `moa.py`. You'll need to: -## Interactive Demo +1. Install the Together Python library: `pip install together` +2. Get your [Together API Key](https://api.together.xyz/settings/api-keys) & export it: `export TOGETHER_API_KEY=` +3. Run the python file: `python moa.py` -We first present an interactive demo. It showcases a simple multi-turn chatbot where the final response is aggregated from various reference models. +```py +# Mixture-of-Agents in 50 lines of code + +``` + +## Interactive CLI Demo + +This interactive CLI demo showcases a simple multi-turn chatbot where the final response is aggregated from various reference models. ### Setup 1. Export Your API Key: - Ensure you have your Together API key and export it as an environment variable: + Ensure you have your [Together API key](https://api.together.xyz/settings/api-keys) and export it as an environment variable: ```bash export TOGETHER_API_KEY={your_key} ``` 2. Install Requirements: - + ```bash pip install -r requirements.txt ``` @@ -42,13 +66,12 @@ To run the interactive demo, execute the following script with Python: python bot.py ``` -The script will prompt you to input instructions interactively. Here's how to use it: +The CLI will prompt you to input instructions interactively: 1. Start by entering your instruction at the ">>>" prompt. 2. The system will process your input using the predefined reference models. 3. It will generate a response based on the aggregated outputs from these models. 4. You can continue the conversation by inputting more instructions, with the system maintaining the context of the multi-turn interaction. -5. enter `exit` to exit the chatbot. ### Configuration @@ -65,7 +88,7 @@ You can configure the demo by specifying the following parameters: ## Evaluation We provide scripts to quickly reproduce some of the results presented in our paper -For convenience, we have included the code from [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval), +For convenience, we have included the code from [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval), [MT-Bench](https://github.com/lm-sys/FastChat), and [FLASK](https://github.com/kaistAI/FLASK), with necessary modifications. 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files changed, 42 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index c1a362c..3fda357 100644 --- a/README.md +++ b/README.md @@ -34,8 +34,47 @@ To get to get started with using MoA in your own apps, see `moa.py`. You'll need 3. Run the python file: `python moa.py` ```py -# Mixture-of-Agents in 50 lines of code - +# Mixture-of-Agents in 50 lines of code – see moa.py +import asyncio +import os +from together import AsyncTogether, Together + +client = Together(api_key=os.environ.get("TOGETHER_API_KEY")) +async_client = AsyncTogether(api_key=os.environ.get("TOGETHER_API_KEY")) +reference_models = [ + "Qwen/Qwen2-72B-Instruct", + "Qwen/Qwen1.5-72B-Chat", + "mistralai/Mixtral-8x22B-Instruct-v0.1", + "databricks/dbrx-instruct", +] +aggregator_model = "mistralai/Mixtral-8x22B-Instruct-v0.1" +aggreagator_system_prompt = "...synthesize these responses into a single, high-quality response... Responses from models:" + +async def run_llm(model): + response = await async_client.chat.completions.create( + model=model, + messages=[{"role": "user", "content": "What are some fun things to do in SF?"}], + temperature=0.7, + max_tokens=100, + ) + return response.choices[0].message.content + +async def main(): + results = await asyncio.gather(*[run_llm(model) for model in reference_models]) + + finalStream = client.chat.completions.create( + model=aggregator_model, + messages=[ + {"role": "system", "content": aggreagator_system_prompt}, + {"role": "user", "content": ",".join(str(element) for element in results)}, + ], + stream=True, + ) + + for chunk in finalStream: + print(chunk.choices[0].delta.content or "", end="", flush=True) + +asyncio.run(main()) ``` ## Interactive CLI Demo diff --git a/moa.py b/moa.py index 6c4201a..31b3801 100644 --- a/moa.py +++ b/moa.py @@ -20,7 +20,7 @@ async def run_llm(model): - """Run a single LLM call with a refernece model.""" + """Run a single LLM call with a reference model.""" response = await async_client.chat.completions.create( model=model, messages=[{"role": "user", "content": user_prompt}], From 0d0bd48ac9aee4a5156d9ba945dbc2b6ab85278e Mon Sep 17 00:00:00 2001 From: Hassan El Mghari Date: Sun, 23 Jun 2024 17:14:55 -0700 Subject: [PATCH 3/3] edited max tokens --- moa.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/moa.py b/moa.py index 31b3801..b25d9c0 100644 --- a/moa.py +++ b/moa.py @@ -25,7 +25,7 @@ async def run_llm(model): model=model, messages=[{"role": "user", "content": user_prompt}], temperature=0.7, - max_tokens=100, + max_tokens=512, ) print(model) return response.choices[0].message.content