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Model: eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3 Source: Original Platform
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README.md
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README.md
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---
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license: mit
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datasets:
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- openai/gsm8k
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- eagle0504/openai-gsm8k-enhanced-using-together-ai-deepseek-train8k-test1k-v1
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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library_name: transformers
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tags:
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- fine-tuned
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- qwen
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- deepseek
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- gsm8k
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- reasoning
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---
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# Qwen 2.5-3B-Instruct Fine-Tuned on OpenAI GSM8K with DeepSeek Augmentation
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## Model Overview
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This model is a fine-tuned version of **Qwen/Qwen2.5-3B-Instruct**, optimized for mathematical reasoning tasks using the **OpenAI GSM8K** dataset. The fine-tuning process enhances the model's ability to generate step-by-step explanations for grade school math problems, incorporating **reasoning augmentation** through DeepSeek. The model improves upon GSM8K’s standard answers by integrating additional contextual reasoning derived from DeepSeek’s small model.
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### Key Features:
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- **Base Model**: Qwen 2.5-3B-Instruct
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- **Fine-Tuned On**: OpenAI GSM8K dataset
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- **Enhancement**: Answer augmentation with reasoning insights from **DeepSeek-V3-Small**
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- **Improved Reasoning**: Model not only provides correct answers but also **augments** explanations with logical steps inspired by DeepSeek’s generative capabilities.
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## Dataset & Training Details
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- **Dataset**: OpenAI’s GSM8K (Grade School Math 8K), a collection of high-quality math problems designed to test problem-solving skills.
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- **Data Curation**: We have provided enhanced OpenAI's GSM8K dataset with reasoning skills generated using DeepSeek-R1, see [here](https://huggingface.co/datasets/eagle0504/openai-gsm8k-enhanced-using-together-ai-deepseek-train8k-test1k-v1).
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- **Enhancement**: After fine-tuning on GSM8K, additional reasoning layers were introduced using **DeepSeek-V3-Small**, leading to richer, more interpretable answers.
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- **Training Objective**: Improve step-by-step mathematical reasoning and **enhance logical deductions** in model-generated responses.
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I have adopted some code from Unsloth and here's an updated [notebook](https://colab.research.google.com/drive/1HV0YkyiTD55j1xLRBHwJ_q3ex82W5EXr?usp=sharing) on Colab. Please feel free to copy it and run it yourself.
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You will need:
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- Huggingface token
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- Together.AI API Key
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- Unsloth package
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## How to Use Model via Terminal (Mac)
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**Goal** Run Qwen-2.5-3B Instruct on Your Mac Using `llama.cpp`
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Yes! You can run **Qwen-2.5-3B Instruct** on your Mac using `llama.cpp`. Here’s a step-by-step guide assuming you are starting from a clean macOS installation with only `pyenv` installed.
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### **Step 1: Install Homebrew (if not installed)**
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Homebrew is required to install `llama.cpp`.
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1. Open **Terminal** (`Cmd + Space`, type `Terminal`, and press **Enter**).
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2. Run:
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```sh
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/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
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```
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3. After installation, add Homebrew to your PATH:
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```sh
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echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> ~/.zprofile
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eval "$(/opt/homebrew/bin/brew shellenv)"
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```
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---
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### **Step 2: Install `llama.cpp` via Homebrew**
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Run:
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```sh
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brew install llama.cpp
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```
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Once installed, you should be able to use `llama-cli`.
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---
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### **Step 3: Run Qwen-2.5-3B Instruct with `llama-cli`**
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To run the model, execute:
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```sh
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llama-cli -hf eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3:Q8_0
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```
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---
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### **Step 4: Additional Configurations (If Needed)**
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If you encounter issues or need finer control, you may want to:
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#### **A. Verify Installation**
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Check if `llama-cli` is installed:
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```sh
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llama-cli --version
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```
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If you see a version output, it’s installed correctly.
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#### **B. Run with Explicit Model Path**
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If the default Hugging Face loader doesn't work, you can manually download the model:
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1. **Create a models directory:**
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```sh
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mkdir -p ~/llama_models && cd ~/llama_models
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```
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2. **Download the GGUF model file** from [Hugging Face](https://huggingface.co/eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3):
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```sh
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wget https://huggingface.co/eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3/blob/main/unsloth.Q8_0.gguf
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```
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3. **Run the model manually**:
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```sh
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llama-cli -m ~/llama_models/Q8_0.gguf
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```
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---
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### **Step 5: Test the Model**
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Try prompting it:
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```sh
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llama-cli -m ~/llama_models/Q8_0.gguf -p "Explain quantum computing in simple terms."
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```
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or interactively:
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```sh
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llama-cli -m ~/llama_models/Q8_0.gguf --interactive
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```
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## How to Use Model via Python
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You can load this model with `transformers`:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# Example prompt
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prompt = "A farmer has 24 apples. He gives 6 to each of his 3 children. How many does he have left?"
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inputs = tokenizer(prompt, return_tensors="pt")
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output = model.generate(**inputs, max_length=200)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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## Expected Performance
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Compared to the base **Qwen2.5-3B-Instruct**, this fine-tuned model:
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- Provides **more detailed explanations** when answering GSM8K math problems.
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- Improves **logical reasoning** by incorporating DeepSeek-style augmented reasoning.
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- Generates **clearer step-by-step solutions**, making it useful for educational or tutoring applications.
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## Model Directory
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The model is hosted on **Hugging Face Hub**:
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👉 **[eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3](https://huggingface.co/eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3)**
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## License
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This model is released under the **MIT License**, allowing open usage and modifications.
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---
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If you have any questions or suggestions for improvements, feel free to reach out!
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🔗 [LinkedIn](https://www.linkedin.com/in/yiqiaoyin/)
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