https://github.com/agora-lab-ai/atom

a suite of finetuned LLMs for atomically precise function calling 🧪
https://github.com/agora-lab-ai/atom

ai artificial-intelligence convolutional-neural-networks function-calling gpt-4 llama llama2 llamacpp ml multi-modal open-source rpa rpc task-automation tool-usage transformer workflow-automation

Last synced: 2 months ago
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a suite of finetuned LLMs for atomically precise function calling 🧪

• Host: GitHub
• URL: https://github.com/agora-lab-ai/atom
• Owner: Agora-Lab-AI
• License: mit
• Created: 2023-09-08T22:11:15.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-07T04:22:28.000Z (4 months ago)
• Last Synced: 2024-10-18T23:14:18.174Z (3 months ago)
• Topics: ai, artificial-intelligence, convolutional-neural-networks, function-calling, gpt-4, llama, llama2, llamacpp, ml, multi-modal, open-source, rpa, rpc, task-automation, tool-usage, transformer, workflow-automation
• Language: Python
• Homepage: https://discord.gg/GYbXvDGevY
• Size: 2.35 MB
• Stars: 12
• Watchers: 1
• Forks: 1
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        
[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# Atom

a suite of finetuned LLMs for atomically precise function calling 🧪

✅ Massive function calling dataset of over 20M samples.

✅ First Model: Atom-Z-Tiny - Zephr trained on 100k samples

✅ Vision function calling coming soon

## Install

You can install the package using pip

```bash

pip install atom-torch

```

## Usage

```python

import torch

from transformers import AutoTokenizer, AutoModelForCausalLM

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained("kye/Atom-Z-Tiny-7B")

model = AutoModelForCausalLM.from_pretrained(

  "kye/Atom-Z-Tiny-7B", 

  trust_remote_code=True, 

).to(device)

task = """

[INST] <>

Available functions:

{

    "name": "generate_password",

    "description": "Generate a random password with specified criteria",

    "parameters": {

        "type": "object",

        "properties": {

            "length": {

                "type": "integer",

                "description": "The length of the password"

            },

            "include_numbers": {

                "type": "boolean",

                "description": "Include numbers in the password"

            },

            "include_special_characters": {

                "type": "boolean",

                "description": "Include special characters in the password"

            }

        },

        "required": [

            "length"

        ]

    }

}

<>

I need a new password. Can you generate one for me? [/INST]

"""

input_ids = tokenizer.encode(task, return_tensors="pt")

output = model.generate(input_ids.to(device), max_length=128, temperature=0.7).cpu()

output_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(output_text)

```

### Training

- To train the models, run `accelerate config` and enable DeepSpeed acceleration. `deepspeed/zero3.json` was the configuration file used for training.0

- Then run accelerate launch `accelerate launch finetune.py`

We're finetuning this [model](https://huggingface.co/NousResearch/Yarn-Llama-2-13b-64k) on this [dataset](https://huggingface.co/datasets/kye/all-lucidrain-code-python-tokenized-65536-1)

`sh./run.sh`

- [Dataset](kye/all-lucidrain-code-python-tokenized-65536-1)

### Citation

```bibtex

@misc{peng2023yarn,

    title={YaRN: Efficient Context Window Extension of Large Language Models}, 

    author={Bowen Peng and Jeffrey Quesnelle and Honglu Fan and Enrico Shippole},

    year={2023},

    eprint={2309.00071},

    archivePrefix={arXiv},

    primaryClass={cs.CL}

}

```