ermiaazarkhalili/Qwen2.5-14B-Instruct_Function_Calling_xLAM

Qwen2.5-14B-Instruct Fine-tuned on xLAM

Overview

This is a fine-tuned version of the Qwen2.5-14B-Instruct model. The model was trained using Hugging Face's TRL library on the xLAM dataset for function calling capabilities.

Model Details

• Developed by: ermiaazarkhalili
• License: cc-by-nc-4.0
• languages: en
• Finetuned from model: Qwen/Qwen2.5-14B-Instruct
• Model size: Qwen2.5-14B-Instruct parameters
• Vocab size: 152,064 tokens
• Max sequence length: 2,048 tokens
• Tensor type: BF16
• Pad token: <|im_end|> (ID: 151645)

Training Information

The model was fine-tuned using the following configuration:

Training Libraries

• Hugging Face TRL Library for advanced training techniques
• LoRA (Low-Rank Adaptation) for parameter-efficient training
• 4-bit quantization for memory efficiency

Training Parameters

• Learning Rate: 0.0001
• Batch Size: 4
• Gradient Accumulation Steps: 8
• Max Training Steps: 1,000
• Warmup Ratio: 0.1
• Max Sequence Length: 2,048
• Output Directory: ./Qwen2_5_14B_Instruct_xLAM

LoRA Configuration

• LoRA Rank (r): 16
• LoRA Alpha: 32
• Target Modules: Query and Value projections
• LoRA Dropout: 0.1

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "ermiaazarkhalili/Qwen2.5-14B-Instruct_Function_Calling_xLAM",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(
    "ermiaazarkhalili/Qwen2.5-14B-Instruct_Function_Calling_xLAM",
    trust_remote_code=True
)

text= "<user>Check if the numbers 8 and 1233 are powers of two.</user>\n\n<tools>"

# Tokenize and generate
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id
)

# Decode response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
generated_text = response[len(text):].strip()
print(generated_text)

Dataset

The model was trained on the xLAM dataset.

Model Performance

This fine-tuned model demonstrates improved capabilities in:

• Function Detection: Identifying when to call functions
• Parameter Extraction: Extracting correct parameters from user queries
• Output Formatting: Generating properly structured function calls
• Tool Integration: Working with external APIs and tools

Credits

This model was developed by ermiaazarkhalili and leverages the capabilities of:

• Qwen2.5-14B-Instruct base model
• Hugging Face TRL for advanced fine-tuning techniques
• LoRA for parameter-efficient adaptation

Contact

For any inquiries or support, please reach out to the developer at ermiaazarkhalili.

Acknowledgments

We would like to thank the creators of:

• Qwen2.5-14B-Instruct for the excellent base model
• Hugging Face for the TRL library and infrastructure
• xLAM dataset contributors
• LoRA researchers for parameter-efficient fine-tuning methods

Citation

If you use this model, please cite:

@misc{ermiaazarkhalili_Qwen2.5-14B-Instruct_Function_Calling_xLAM,
author = {ermiaazarkhalili},
title = { Fine-tuning  Qwen2.5-14B-Instruct on xLAM for Function Calling},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/ermiaazarkhalili/Qwen2.5-14B-Instruct_Function_Calling_xLAM}}
}