Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language:
+- en
+tags:
+- math
+- education
+- llama-3
+- peft
+- lora
+base_model: meta-llama/Llama-3.2-1B-Instruct
+license: apache-2.0
+---
+
+# NexusLLM-Math-1B-v1
+
+## Model Details
+NexusLLM-Math-1B-v1 is a fine-tuned version of Llama 3.2 (1B parameters) optimized specifically for solving advanced high-school mathematics problems, with a focus on JEE Main and Advanced syllabus topics. 
+
+- **Developed by:** ZentithLLM
+- **Model Type:** Causal Language Model (Fine-tuned with LoRA)
+- **Language:** English
+- **Base Model:** meta-llama/Llama-3.2-1B-Instruct
+- **Precision:** FP16
+
+## Intended Use
+This model is designed to act as an educational assistant for 11th-grade mathematics. It is trained to provide step-by-step reasoning and explanations for complex topics, rather than just outputting the final answer.
+
+**Primary Topics Covered:**
+- Binomial Theorem
+- Geometry (Circle Theorems, cyclic quadrilaterals, tangents, etc.)
+
+## Training Data
+The model was trained on a custom dataset of structured mathematics Q&A pairs. The dataset maps specific mathematical prompts to detailed completions, heavily utilizing an `explanation` field to teach the model the underlying mathematical logic and derivation steps.
+
+## Training Procedure
+The model was fine-tuned using the standard Hugging Face `trl` and `peft` libraries on a single NVIDIA T4 GPU, utilizing strictly native FP16 precision to ensure mathematical gradient stability.
+
+- **Training Framework:** Pure Hugging Face (No Unsloth/Quantization)
+- **Method:** LoRA (Low-Rank Adaptation)
+- **Rank (r):** 32
+- **Alpha:** 32
+- **Optimizer:** adamw_torch
+- **Learning Rate:** 2e-4
+- **Max Sequence Length:** 2048
+
+## How to Use
+Because this model was trained on a specific dataset structure, you **must** wrap your prompts in the `### Instruction:` and `### Response:` format for it to output the correct mathematical explanations.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "ZentithLLM/NexusLLM-Math-1B-v1"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto" 
+)
+
+question = "What is the general term in the expansion of (x+y)^n?"
+formatted_prompt = f"### Instruction:\\n{question}\\n\\n### Response:\\n"
+
+inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=250,
+    temperature=0.3,
+    do_sample=True,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))