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README.md

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+# 🧠 Phi-4 Reasoning -- Rust Dataset LoRA (Merged)
+
+This repository contains a fine-tuned version of
+**unsloth/phi-4-reasoning**, trained with **LoRA** on the
+**Tesslate/Rust_Dataset**.\
+The goal of this project is to enhance the model's reasoning,
+explanation, and step-by-step thinking abilities specifically for
+**Rust-related tasks**.
+
+## 🚀 Model Purpose
+
+This model was fine-tuned to:
+
+-   Improve **Rust coding explanations**\
+-   Generate **high-quality reasoning traces**\
+-   Provide **step-by-step problem solving**\
+-   Give **detailed and structured answers**\
+-   Handle **`<think>`{=html}...`</think>`{=html} hidden reasoning
+    tags**
+
+The training format follows:
+
+    <|user|>
+    {prompt}
+    <|assistant|>
+    <think>
+    {reasoning}
+    </think>
+    {response}
+
+## 🧩 Base Model
+
+**unsloth/phi-4-reasoning**
+
+-   14B parameter reasoning-optimized model\
+-   Uses internal `<think>` reasoning\
+-   Strong on step-by-step chain-of-thought tasks
+
+## 🛠 Fine-Tuning Details
+
+  Setting          Value
+  ---------------- -----------------------------------------
+  Method           LoRA (PEFT)
+  Rank (r)         16
+  Alpha            32
+  Dropout          0.05
+  Target Modules   q/k/v/o proj, mlp (up/down/gate)
+  Max Length       2048
+  Precision        4-bit QLoRA (merged later to BF16/FP16)
+  Batch Size       4
+  Grad Accum       8
+  LR               2e-4
+  Scheduler        cosine
+  Epochs           2
+
+## 📚 Dataset
+
+**Tesslate/Rust_Dataset**
+
+Includes:
+
+-   Rust prompts\
+-   Step-by-step reasoning\
+-   Final answers
+
+This dataset improves the model's ability to produce structured and
+accurate explanations for Rust programming tasks.
+
+## 🔧 How to Use
+
+### Load model normally:
+
+``` python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "YOUR_USERNAME/YOUR_MODEL_NAME"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+prompt = "Explain ownership in Rust with examples."
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=300)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## 🔍 Notes on Reasoning Tags
+
+This model preserves **hidden reasoning structure**:
+
+-   `<think>` content is **internal chain-of-thought**\
+-   The final output is **placed after the reasoning block**
+
+⚠️ Users should NOT expect the `<think>` content to be revealed; the
+model is aligned to hide reasoning by default.
+
+## 📦 Files Included
+
+-   `config.json`\
+-   `generation_config.json`\
+-   `pytorch_model.bin`\
+-   `tokenizer.json`
+
+If this is a LoRA-only repo (not merged), then the repo contains:
+
+-   `adapter_config.json`\
+-   `adapter_model.bin`
+
+## 🔒 License
+
+This model inherits the license of the base model:\
+**Microsoft Phi License / Reasoning Model Terms**
+
+## ✨ Acknowledgements
+
+-   **Unsloth** for optimized model training\
+-   **HuggingFace Transformers & PEFT** team\
+-   **Tesslate** for providing the Rust dataset