Update README.md

README.md

@@ -6,11 +6,108 @@ tags:
 - unsloth
 - qwen3
 - trl
+- qwen-3
+- fine-tuning
+- openmathreasoning
+- python
+- unsloth
+- lora
+- peft
+- tutorial
+- reasoning
+- chat
 license: apache-2.0
 language:
 - en
 ---
 
+# krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged
+
+🚀 **Harness the Power of Qwen-3 with Enhanced Reasoning and Chat!** 🚀
+
+This model is a carefully fine-tuned version of the incredible [Qwen-3-8B](https://huggingface.co/Qwen/Qwen3-8B) using cutting-edge techniques with [Unsloth](https://github.com/unslothai/unsloth) and Parameter-Efficient Fine-Tuning (PEFT) via LoRA. It's designed to bring you the best of both worlds: the strong general capabilities of Qwen-3 with a significant boost in logical reasoning and engaging conversational skills.
+
+We've taken the already powerful Qwen-3 and further sculpted it using a blend of the [unsloth/OpenMathReasoning-mini](https://huggingface.co/datasets/unsloth/OpenMathReasoning-mini) (Chain-of-Thought split) for advanced problem-solving and the [mlabonne/FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset to ensure natural and fluent interactions.
+
+**🔥 Key Features:**
+
+* **Enhanced Reasoning:** Excels at tasks requiring logical deduction and step-by-step thinking, thanks to fine-tuning on a dedicated reasoning dataset.
+* **Improved Chat:** Maintains and enhances the general conversational abilities of Qwen-3, making it great for interactive applications.
+* **Efficient Fine-Tuning:** Built using the incredibly efficient [Unsloth](https://github.com/unslothai/unsloth) library, resulting in faster training with less memory usage.
+* **PEFT (LoRA) Inside:** Leverages Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning, making it easier to adapt to specific tasks without full model retraining.
+* **Ready to Use:** Seamlessly integrates with the `transformers` library.
+
+
+**🛠️ How to Get Started:**
+
+Install the necessary libraries:
+
+```bash
+pip install transformers accelerate torch
+```
+
+Load and use the model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="torch.float16")
+
+messages = [
+    {"role": "user", "content": "Explain the Pythagorean theorem in simple terms."},
+    {"role": "assistant", "content": "Okay, here's a simple explanation:"},
+    {"role": "user", "content": "Now, solve for the hypotenuse if a=3 and b=4."},
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.8, top_k=20, do_sample=True)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+**⚙️ Fine-tuning Details:**
+
+* **Base Model:** [Qwen-3-8B](https://huggingface.co/Qwen/Qwen3-8B)
+* **Fine-tuning Framework:** [Unsloth](https://github.com/unslothai/unsloth)
+* **PEFT Strategy:** LoRA
+* **Training Datasets:**
+    * [unsloth/OpenMathReasoning-mini](https://huggingface.co/datasets/unsloth/OpenMathReasoning-mini) (COT split)
+    * [mlabonne/FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k)
+* **Training Ratio:** Approximately 30% reasoning data and 70% general chat data to balance capabilities.
+* **Training Infrastructure:** Google Colab with a T4 GPU.
+* **Quantization during Training:** Likely 4-bit quantization was employed during the fine-tuning process using Unsloth for memory efficiency. The final merged model is saved in 16-bit for broader compatibility.
+* **Key Hyperparameters:**
+    * `per_device_train_batch_size`: 2
+    * `gradient_accumulation_steps`: 4
+    * `learning_rate`: 2e-4
+    * `max_steps`: 30
+    * Optimizer: `adamw_8bit`
+    * Learning Rate Scheduler: `linear`
+    * Warmup Steps: 5
+    * Weight Decay: 0.01
+    * Seed: 3407
+
+**📊 Evaluation:**
+
+While rigorous quantitative evaluations are ongoing, initial assessments indicate a significant improvement in the model's ability to handle reasoning-based questions while maintaining strong general conversational skills. Further benchmarks and community feedback are welcome!
+
+**👨‍💻 Author:**
+
+[https://huggingface.co/krishanwalia30]
+
+**🔗 Learn More:**
+
+For a deeper dive into the fine-tuning process and the rationale behind the choices, check out the article: [https://medium.com/@krishanw30/b1a8f684c3f1].
+
+**🙏 Acknowledgements:**
+
+A big thank you to the brilliant teams at [Qwen](https://huggingface.co/Qwen), [Unsloth AI](https://github.com/unslothai/unsloth), and the creators of the [OpenMathReasoning-mini](https://huggingface.co/datasets/unsloth/OpenMathReasoning-mini) and [FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) datasets for making this project possible!
+
+
 # Uploaded  model
 
 - **Developed by:** krishanwalia30