eagle0504
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qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v4

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 ---
 library_name: transformers
 tags:
 - unsloth
 - trl
 - grpo
 ---
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: mit
+datasets:
+- eagle0504/openai-gsm8k-enhanced-using-together-ai-deepseek-train8k-test1k-v1
+language:
+- en
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
 library_name: transformers
 tags:
+- fine-tuned
 - unsloth
 - trl
 - grpo
+- deepseek
+- gsm8k
+- reasoning
 ---
+# **DeepSeek-R1-Distill-Qwen-1.5B Fine-Tuned on GSM8K with Chain-of-Thought Augmentation**
+## **Model Overview**
+This model is a fine-tuned version of **DeepSeek-R1-Distill-Qwen-1.5B**, trained on the **OpenAI GSM8K dataset**, augmented with **Chain-of-Thought (CoT) reasoning** using **DeepSeek-V3**. The fine-tuning process enhances the model’s **mathematical problem-solving abilities**, allowing it to provide **step-by-step solutions** with deeper reasoning.
+### **🔹 Key Features**
+- **Base Model**: DeepSeek-R1-Distill-Qwen-1.5B
+- **Fine-Tuned On**: GSM8K dataset with DeepSeek-V3-enhanced reasoning
+- **Improved Mathematical Reasoning**: Generates detailed step-by-step CoT explanations
+- **Optimized for GRPO Training**: Trained using `trl` and `unsloth` for efficient fine-tuning
+---
+## **📊 Dataset & Training Details**
+- **Dataset**: `eagle0504/openai-gsm8k-enhanced-using-together-ai-deepseek-train8k-test1k-v1`
+  - **8K train samples**, **1K test samples**
+  - Contains **question**, **answer**, and **CoT reasoning**
+- **Training Methodology**:
+  - Used **Guided Reinforcement Policy Optimization (GRPO)** via `trl`
+  - Applied **gradient accumulation** to manage larger batch sizes
+  - Integrated **DeepSeek-V3 augmentation** for enhanced logical reasoning
+- **Fine-tuning Tools**:
+  - **Unsloth** for memory-efficient Llama-based tuning
+  - **Hugging Face Transformers** for model training
+For those interested in replicating the fine-tuning process, I have shared an **updated Colab notebook** 📓:
+🔗 [Colab Notebook](https://colab.research.google.com/drive/1HV0YkyiTD55j1xLRBHwJ_q3ex82W5EXr?usp=sharing)
+You will need:
+✅ Hugging Face Token
+✅ Together.AI API Key
+✅ Unsloth Package
+---
+## **🚀 How to Run the Model (Mac via `llama.cpp`)**
+Yes! You can run this model **locally on macOS** using `llama.cpp`.
+### **1️⃣ Install Homebrew (If Not Installed)**
+```sh
+/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
+```
+Then add Homebrew to your PATH:
+```sh
+echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> ~/.zprofile
+eval "$(/opt/homebrew/bin/brew shellenv)"
+```
+---
+### **2️⃣ Install `llama.cpp`**
+```sh
+brew install llama.cpp
+```
+---
+### **3️⃣ Run the Model with `llama-cli`**
+```sh
+llama-cli -hf eagle0504/deepseek-r1-qwen-1.5b-gsm8k-enhanced-gguf:Q8_0
+```
+---
+### **4️⃣ Alternative: Run Locally via GGUF**
+```sh
+mkdir -p ~/llama_models && cd ~/llama_models
+wget https://huggingface.co/eagle0504/deepseek-r1-qwen-1.5b-gsm8k-enhanced-gguf/resolve/main/Q8_0.gguf
+llama-cli -m ~/llama_models/Q8_0.gguf --interactive
+```
+---
+## **📌 How to Use Model via Python (`transformers`)**
+You can load the model with **Hugging Face Transformers**:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "eagle0504/deepseek-r1-qwen-1.5b-gsm8k-enhanced"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+prompt = "A farmer has 24 apples. He gives 6 to each of his 3 children. How many does he have left?"
+inputs = tokenizer(prompt, return_tensors="pt")
+output = model.generate(**inputs, max_length=200)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+---
+## **🔬 Expected Performance**
+Compared to the base **DeepSeek-R1-Distill-Qwen-1.5B**, this fine-tuned model:
+- Provides **more detailed Chain-of-Thought (CoT) explanations** for GSM8K problems.
+- Improves **logical reasoning and step-by-step answer formulation**.
+- Generates **clearer, more structured solutions**, making it **ideal for educational use**.
+---
+## **🗂 Model Hosting & License**
+📌 **Model on Hugging Face Hub**:
+👉 **[eagle0504/deepseek-r1-qwen-1.5b-gsm8k-enhanced](https://huggingface.co/eagle0504/deepseek-r1-qwen-1.5b-gsm8k-enhanced)**
+📜 **License**: MIT License – Open for modification and distribution.
+---
+If you have **feedback or ideas for improvement**, feel free to reach out! 🚀🔥
+#AI #MachineLearning #DeepSeek #GSM8K #LLM #ChainOfThought #HuggingFace #GRPO #Reasoning
+```