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Model: Khurram123/Llama-3.2-3B-Calculus-v2
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model: unsloth/Llama-3.2-3B-Instruct
+tags:
+- calculus
+- mathematics
+- educational
+- unsloth
+- qlora
+language:
+- en
+pipeline_tag: text-generation
+library_name: unsloth
+---
+
+<div align="center">
+
+# Llama-3.2-3B-Calculus-v2
+
+$$\displaystyle \int f(x) \, dx \quad \text{and} \quad \frac{d}{dx} f(x)$$
+
+**Specialized Fine-Tuned Model for Mathematical Reasoning**
+
+[![Model License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Unsloth](https://img.shields.io/badge/Optimized%20by-Unsloth-orange.svg)](https://github.com/unslothai/unsloth)
+
+</div>
+
+---
+
+## 📌 Overview
+**Llama-3.2-3B-Calculus-v2** is a specialized large language model fine-tuned for mathematical reasoning, specifically targeting **Differential and Integral Calculus**. Developed by **Khurram Pervez**, this model utilizes Chain-of-Thought (CoT) prompting to break down complex mathematical problems into logical, pedagogical steps.
+
+The model was fine-tuned on an **NVIDIA GeForce RTX 4060 Ti 16GB** using 4-bit quantization (QLoRA) to maximize efficiency while maintaining high reasoning accuracy over 500 training steps.
+
+---
+
+## 🚀 Key Features
+* **Step-by-Step Derivations:** Optimized to explain the "why" behind each calculus rule.
+* **Rule-Based Reasoning:** Trained to identify and apply the Product Rule, Chain Rule, and Integration by Parts.
+* **Calculus Specialist:** Targeted performance on Taylor Series expansions, limits, and transcendental functions.
+* **Efficient Local AI:** Designed to run on consumer-grade hardware with minimal VRAM.
+
+---
+
+## 🛠️ Training Technicalities
+* **Base Model:** `unsloth/Llama-3.2-3B-Instruct`
+* **Fine-tuning Method:** QLoRA (Rank: 32, Alpha: 32)
+* **Steps:** 500 Steps
+* **Final Train Loss:** 0.4789
+* **Optimizer:** 8-bit AdamW
+* **Scheduler:** Cosine Decay
+* **Hardware:** Local Ubuntu Workstation (NVIDIA RTX 4060 Ti 16GB)
+
+---
+
+## 📖 Usage Instructions
+
+### Simple Inference
+You can run this model using the `unsloth` library for 2x faster inference. We recommend a low temperature (0.1) for mathematical stability.
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+# 1. Load Model and Tokenizer
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "Khurram123/Llama-3.2-3B-Calculus-v2",
+    max_seq_length = 2048,
+    load_in_4bit = True,
+)
+FastLanguageModel.for_inference(model)
+
+# 2. Define Calculus Problem
+problem = "Find the derivative of f(x) = x^2 * ln(x) step by step."
+
+# 3. Apply Llama 3.2 Instruct Template
+messages = [{"role": "user", "content": problem}]
+inputs = tokenizer.apply_chat_template(
+    messages, 
+    add_generation_prompt = True, 
+    return_tensors = "pt"
+).to("cuda")
+
+# 4. Generate Solution
+outputs = model.generate(
+    input_ids = inputs, 
+    max_new_tokens = 1024, 
+    temperature = 0.1
+)
+
+# 5. Decode Output
+response = tokenizer.decode(outputs[0], skip_special_tokens = True)
+print(response.split("assistant")[-1].strip())
+```
+---
+
+## 📊 Dataset Reference
+The model was fine-tuned using a filtered subset of the **MathInstruct** dataset, focusing specifically on calculus-related instructional pairs to enhance symbolic manipulation and logical derivation.
+
+- **Dataset Name:** [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
+- **Paper:** [MathInstruct: A Compiled Institution Dataset for Mathematical Reasoning](https://arxiv.org/abs/2309.04408)
+- **Source:** TIGER-Lab (University of Waterloo, Ohio State University, et al.)
+
+### Citation
+If you use this model or the underlying data, please cite the original MathInstruct paper:
+
+```bibtex
+@article{yue2023mathinstruct,
+  title={Mathinstruct: A compiled instruction dataset for mathematical reasoning},
+  author={Yue, Xiang and Qu, Xingwei and Zhang, Ge and Yao, Liang and Huo, Shijie and Sun, Wei and Caswell, Isaac and Xie, Wenhu and others},
+  journal={arXiv preprint arXiv:2309.04408},
+  year={2023}
+}

config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 128009,
+    "head_dim": 128,
+    "hidden_act": "silu",
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+    "initializer_range": 0.02,
+    "intermediate_size": 8192,
+    "max_position_embeddings": 131072,
+    "mlp_bias": false,
+    "model_type": "llama",
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+    "pretraining_tp": 1,
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+    "rope_scaling": {
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+        "high_freq_factor": 4.0,
+        "low_freq_factor": 1.0,
+        "original_max_position_embeddings": 8192,
+        "rope_type": "llama3"
+    },
+    "rope_theta": 500000.0,
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.3.17",
+    "use_cache": true,
+    "vocab_size": 128256
+}

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special_tokens_map.json

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