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Model: Khurram123/Llama-3.2-3B-Calculus-v2 Source: Original Platform
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---
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license: apache-2.0
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base_model: unsloth/Llama-3.2-3B-Instruct
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tags:
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- calculus
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- mathematics
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- educational
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- unsloth
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- qlora
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language:
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- en
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pipeline_tag: text-generation
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library_name: unsloth
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---
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<div align="center">
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# Llama-3.2-3B-Calculus-v2
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$$\displaystyle \int f(x) \, dx \quad \text{and} \quad \frac{d}{dx} f(x)$$
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**Specialized Fine-Tuned Model for Mathematical Reasoning**
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://github.com/unslothai/unsloth)
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</div>
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---
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## 📌 Overview
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**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.
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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.
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---
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## 🚀 Key Features
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* **Step-by-Step Derivations:** Optimized to explain the "why" behind each calculus rule.
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* **Rule-Based Reasoning:** Trained to identify and apply the Product Rule, Chain Rule, and Integration by Parts.
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* **Calculus Specialist:** Targeted performance on Taylor Series expansions, limits, and transcendental functions.
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* **Efficient Local AI:** Designed to run on consumer-grade hardware with minimal VRAM.
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---
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## 🛠️ Training Technicalities
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* **Base Model:** `unsloth/Llama-3.2-3B-Instruct`
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* **Fine-tuning Method:** QLoRA (Rank: 32, Alpha: 32)
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* **Steps:** 500 Steps
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* **Final Train Loss:** 0.4789
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* **Optimizer:** 8-bit AdamW
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* **Scheduler:** Cosine Decay
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* **Hardware:** Local Ubuntu Workstation (NVIDIA RTX 4060 Ti 16GB)
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---
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## 📖 Usage Instructions
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### Simple Inference
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You can run this model using the `unsloth` library for 2x faster inference. We recommend a low temperature (0.1) for mathematical stability.
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```python
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from unsloth import FastLanguageModel
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import torch
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# 1. Load Model and Tokenizer
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model, tokenizer = FastLanguageModel.from_pretrained(
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"Khurram123/Llama-3.2-3B-Calculus-v2",
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max_seq_length = 2048,
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load_in_4bit = True,
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)
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FastLanguageModel.for_inference(model)
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# 2. Define Calculus Problem
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problem = "Find the derivative of f(x) = x^2 * ln(x) step by step."
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# 3. Apply Llama 3.2 Instruct Template
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messages = [{"role": "user", "content": problem}]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt = True,
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return_tensors = "pt"
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).to("cuda")
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# 4. Generate Solution
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outputs = model.generate(
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input_ids = inputs,
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max_new_tokens = 1024,
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temperature = 0.1
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)
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# 5. Decode Output
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response = tokenizer.decode(outputs[0], skip_special_tokens = True)
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print(response.split("assistant")[-1].strip())
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```
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---
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## 📊 Dataset Reference
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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.
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- **Dataset Name:** [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
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- **Paper:** [MathInstruct: A Compiled Institution Dataset for Mathematical Reasoning](https://arxiv.org/abs/2309.04408)
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- **Source:** TIGER-Lab (University of Waterloo, Ohio State University, et al.)
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### Citation
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If you use this model or the underlying data, please cite the original MathInstruct paper:
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```bibtex
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@article{yue2023mathinstruct,
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title={Mathinstruct: A compiled instruction dataset for mathematical reasoning},
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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},
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journal={arXiv preprint arXiv:2309.04408},
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year={2023}
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}
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"torch_dtype": "bfloat16",
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"eos_token_id": 128009,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 24,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"unsloth_fixed": true,
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"unsloth_version": "2026.3.17",
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"use_cache": true,
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"vocab_size": 128256
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}
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||||
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||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2067
tokenizer_config.json
Normal file
2067
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user