Update README.md

README.md

@@ -5,17 +5,74 @@ tags:
 - transformers
 - unsloth
 - gpt_oss
+- mathematics
+- olympiad-math
+- reasoning
+- chain-of-thought
 license: apache-2.0
 language:
 - en
+datasets:
+- HuggingFaceH4/Multilingual-Thinking
+- brando/olympiad-bench-imo-math-boxed-825-v2-21-08-2024
+- Goedel-LM/MathOlympiadBench
+- hf-imo-colab/olympiads-ref-base-math-word
 ---
 
-# Uploaded finetuned  model
+# GPT-OSS-120B Olympiad Reasoning
 
-- **Developed by:** Azmainadeeb
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/gpt-oss-120b-unsloth-bnb-4bit
+This model is a specialized fine-tune of **OpenAI's GPT-OSS 120B** (4-bit quantized by Unsloth). It is designed for high-level mathematical reasoning, complex problem solving, and long-form "Thinking" processes.
 
-This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+- **Developed by:** Azmainadeeb
+- **Base Model:** unsloth/gpt-oss-120b-unsloth-bnb-4bit
+- **Architecture:** Mixture-of-Experts (MoE) with 117B total and 5.1B active parameters.
+- **License:** Apache-2.0
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+## 🌟 Model Highlights
+This model uses the **Harmony Response Format** natively, allowing for a distinct separation between "internal reasoning" and "final response." By fine-tuning on a mixture of thinking traces and competition-grade math, the model exhibits improved logical consistency and accuracy in STEM domains.
+
+### Capabilities:
+* **Deep Reasoning:** Leverages the `Multilingual-Thinking` dataset to maintain a coherent chain-of-thought.
+* **Competition Math:** Optimized for International Mathematical Olympiad (IMO) and AIME-style problems.
+* **Variable Effort:** Supports the `reasoning_effort` parameter (low, medium, high) to balance speed and accuracy.
+
+
+
+## 📊 Training Data
+The model was trained on a high-diversity mixture of reasoning and mathematical datasets:
+
+1. **[HuggingFaceH4/Multilingual-Thinking](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking):** Provides the foundational "thinking" behavior and internal monologue.
+2. **[brando/olympiad-bench-imo-math](https://huggingface.co/datasets/brando/olympiad-bench-imo-math-boxed-825-v2-21-08-2024):** High-difficulty math competition problems.
+3. **[Goedel-LM/MathOlympiadBench](https://huggingface.co/datasets/Goedel-LM/MathOlympiadBench):** Challenging math benchmark problems.
+4. **[hf-imo-colab/olympiads-ref-base-math-word](https://huggingface.co/datasets/hf-imo-colab/olympiads-ref-base-math-word):** Diverse word problems and solutions.
+5. **Kaggle External Math Data:** Curated datasets from AoPS, AIMO, and OlympiadBench for extra-domain coverage.
+
+## 🛠 Usage Instructions
+This model is optimized for use with the **Unsloth** library and Hugging Face's `transformers`. 
+
+### Quick Inference Example
+```python
+from unsloth import FastLanguageModel
+import torch
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "Azmainadeeb/gpt-oss-120b-olympiad", # Replace with your repo ID
+    max_seq_length = 2048,
+    load_in_4bit = True,
+)
+
+messages = [
+    {"role": "user", "content": "Let n be a positive integer such that n^2 + 3n + 2 is a perfect square. Find all such n."}
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt = True,
+    reasoning_effort = "medium", # Options: low, medium, high
+    return_tensors = "pt"
+).to("cuda")
+
+outputs = model.generate(input_ids, max_new_tokens = 1024)
+print(tokenizer.decode(outputs[0]))