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
You can install the package using pip
pip install atom-torchimport 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] <<SYS>>
<function>Available functions:
<function>{
"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"
]
}
}
<</SYS>>
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)
- To train the models, run
accelerate configand enable DeepSpeed acceleration.deepspeed/zero3.jsonwas the configuration file used for training.0 - Then run accelerate launch
accelerate launch finetune.py
We're finetuning this model on this dataset
sh./run.sh
@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}
}