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---
license: apache-2.0
base_model: meta-llama/Meta-Llama-3.1-70B-Instruct
tags:
- function
- function-calling
- tool-using
---
## Empower Functions Model v1.1
[https://github.com/empower-ai/empower-functions](https://github.com/empower-ai/empower-functions)
Empower Functions is a family of LLMs(large language models) that offer GPT-4 level capabilities for real-world "tool using" use cases, with full compatibility support to serve as a drop-in replacement.
## Key Features
* Automatic tool using, able to decide when to use tools and when to converse, optimized for long conversations
* Parallel call, supports calling one function multiple times, multiple functions, or a combination of both
* Sequential calling, supports calling multiple functions sequentially to fulfill the user request
* Streaming
## Family of Models
| Model | Specs | Links | Notes |
| ------------------------------ | ------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------- |
| llama3-empower-functions-small | 128k context, based on [Llama3.1 8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) | [model](https://huggingface.co/empower-dev/llama3-empower-functions-small-v1.1), [gguf](https://huggingface.co/empower-dev/llama3-empower-functions-small-gguf-v1.1) | Most cost-effective, locally runnable | | Balance in accuracy and cost |
| llama3-empower-functions-large | 128k context, based on [Llama3.1 70B](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B) | [model](https://huggingface.co/empower-dev/llama3-empower-functions-large-v1.1) | Best accuracy |
### Hardware Requirement
We have tested and the family of models in following setup:
- empower-functions-small: fp16 on 1xA100 40G, GGUF and 4bit GGUF on Macbook M2 Pro with 32G RAM, in minimal the 4bit GGUF version requires 7.56G RAM.
- empower-functions-medium: fp16 on 2xA100 80G
- empower-functions-large: fp16 on 4xA100 80G
## Usage
There are three ways to use the empower-functions model. You can either directly [prompt the raw model](https://github.com/empower-ai/empower-functions?tab=readme-ov-file#prompt-raw-model), run it [locally](https://github.com/empower-ai/empower-functions?tab=readme-ov-file#running-locally) through llama-cpp-python, or use our [hosted API](https://github.com/empower-ai/empower-functions?tab=readme-ov-file#using-empower-api)
## Evaluation
v1.1 is the newer version trained based on meta llama3.1 with the newly updated dataset, it has achieved state-of-the-art performance on the Berkeley Function Calling leaderboard:
(captured on Sep 10, 2024)
## Demo App
Check our healthcare appointment booking [demo](https://app.empower.dev/chat-demo)
Want to customize the model? Please contact us at [founders@empower.dev](mailto:founders@empower.dev) |