README.md

---
license: apache-2.0
datasets:
- Salesforce/xlam-function-calling-60k
language:
- en
metrics:
- accuracy
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
library_name: transformers
tags:
- AIFunctionCall
- ToolCall
- APICall
- AgentCall
- AgentCallTool
- AIAgent
---
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
---

# Model Card for Model ID

This code fine-tunes Mistral-7B-Instruct 🧠 using the Salesforce/xlam-function-calling-60k dataset to improve its ability to generate accurate structured function calls. It loads the dataset 📂, dynamically removes unnecessary columns like "query" and "answers" for cleaner data, and splits it into 90% training and 10% test for evaluation. The preprocess() function structures data in JSON format 📝, enhancing the model’s reasoning through Chain-of-Thought (CoT) prompting. Special tokens like <tools> and <think> are added to guide structured outputs 🔧. The model is further optimized with bnb_4bit quantization for reduced size (~4.5GB) and improved inference efficiency 🚀. The result is a powerful model that can handle complex API requests with improved accuracy and stability. 🔍

## Model Details
This code implements a well-structured process for fine-tuning the Mistral-7B-Instruct model using the Salesforce/xlam-function-calling-60k dataset. The goal is to improve the model’s ability to:

✅ Accurately understand user queries
✅ Generate precise function calls in structured JSON format
✅ Leverage Chain-of-Thought (CoT) reasoning for step-by-step function generation

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [Ritvik Gaur]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [mistralai/Mistral-7B-Instruct-v0.3]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
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## Uses

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Generated Output:\n", generated_text)

### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

pip install transformers torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Link copy and Paste from Ritvik's repo from huggingface
model_name = "ritvik77/FineTune_LoRA__AgentToolCall_Mistral-7B_Transformer"
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Model lOadning
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,  # Efficient for GPU
    device_map="auto"  # Automatically distribute across GPU/CPU
)
# Set to evaluation mode
model.eval()

[More Information Needed]

## Training Details

### Training Data

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[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

 
Hyperparameter	Value	Description
Base Model	mistralai/Mistral-7B-Instruct-v0.3	Foundation model for fine-tuning

Fine-Tuning Method	LoRA (Low-Rank Adaptation)	Efficiently trains only a small subset of parameters

LoRA Rank Dimension	128	Controls the size of trainable LoRA layers

LoRA Alpha	128	Scaling factor for LoRA layers

LoRA Dropout	0.1	Adds regularization to improve model generalization

Train Batch Size	2	Balanced for stable performance on A100 (40GB VRAM)

Eval Batch Size	2	Ensures consistent evaluation during training

Gradient Accumulation Steps	8	Maintains an effective batch size of 16

Learning Rate	2e-4	Optimized for stable convergence

Warmup Ratio	0.1	Gradual learning rate increase for smoother training

Weight Decay	0.1	Prevents overfitting by penalizing large weights

Max Gradient Norm	1.0	Limits gradient spikes for stable training

Number of Epochs	2	Balanced performance without overfitting

Learning Rate Scheduler	Cosine	Provides smoother convergence

Quantization	bnb_4bit	Reduces model size while preserving performance

Precision	fp16	Optimized for modern GPUs like A100/4090

Gradient Checkpointing	Enabled	Reduces memory usage during backpropagation

Optimizer	adamw_bnb_8bit	Efficient optimizer for quantized models
<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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