eagle0504
/

qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3

@@ -1,161 +1,173 @@
----
-license: mit
-datasets:
-- openai/gsm8k
-- eagle0504/openai-gsm8k-enhanced-using-together-ai-deepseek-train8k-test1k-v1
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
-library_name: transformers
-tags:
-- fine-tuned
-- qwen
-- deepseek
-- gsm8k
-- reasoning
----
-# Qwen 2.5-3B-Instruct Fine-Tuned on OpenAI GSM8K with DeepSeek Augmentation
-## Model Overview
-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.
-### Key Features:
-- **Base Model**: Qwen 2.5-3B-Instruct
-- **Fine-Tuned On**: OpenAI GSM8K dataset
-- **Enhancement**: Answer augmentation with reasoning insights from **DeepSeek-V3-Small**
-- **Improved Reasoning**: Model not only provides correct answers but also **augments** explanations with logical steps inspired by DeepSeek’s generative capabilities.
-## Dataset & Training Details
-- **Dataset**: OpenAI’s GSM8K (Grade School Math 8K), a collection of high-quality math problems designed to test problem-solving skills.
-- **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).
-- **Enhancement**: After fine-tuning on GSM8K, additional reasoning layers were introduced using **DeepSeek-V3-Small**, leading to richer, more interpretable answers.
-- **Training Objective**: Improve step-by-step mathematical reasoning and **enhance logical deductions** in model-generated responses.
-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.
-You will need:
-- Huggingface token
-- Together.AI API Key
-- Unsloth package
-## How to Use Model via Terminal (Mac)
-**Goal** Run Qwen-2.5-3B Instruct on Your Mac Using `llama.cpp`
-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.
-### **Step 1: Install Homebrew (if not installed)**
-Homebrew is required to install `llama.cpp`.
-1. Open **Terminal** (`Cmd + Space`, type `Terminal`, and press **Enter**).
-2. Run:
-   ```sh
-   /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
-   ```
-3. After installation, add Homebrew to your PATH:
-   ```sh
-   echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> ~/.zprofile
-   eval "$(/opt/homebrew/bin/brew shellenv)"
-   ```
----
-### **Step 2: Install `llama.cpp` via Homebrew**
-Run:
-```sh
-brew install llama.cpp
-```
-Once installed, you should be able to use `llama-cli`.
----
-### **Step 3: Run Qwen-2.5-3B Instruct with `llama-cli`**
-To run the model, execute:
-```sh
-llama-cli -hf eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3:Q8_0
-```
----
-### **Step 4: Additional Configurations (If Needed)**
-If you encounter issues or need finer control, you may want to:
-#### **A. Verify Installation**
-Check if `llama-cli` is installed:
-```sh
-llama-cli --version
-```
-If you see a version output, it’s installed correctly.
-#### **B. Run with Explicit Model Path**
-If the default Hugging Face loader doesn't work, you can manually download the model:
-1. **Create a models directory:**
-   ```sh
-   mkdir -p ~/llama_models && cd ~/llama_models
-   ```
-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):
-   ```sh
-   wget https://huggingface.co/eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3/blob/main/unsloth.Q8_0.gguf
-   ```
-3. **Run the model manually**:
-   ```sh
-   llama-cli -m ~/llama_models/Q8_0.gguf
-   ```
----
-### **Step 5: Test the Model**
-Try prompting it:
-```sh
-llama-cli -m ~/llama_models/Q8_0.gguf -p "Explain quantum computing in simple terms."
-```
-or interactively:
-```sh
-llama-cli -m ~/llama_models/Q8_0.gguf --interactive
-```
-## How to Use Model via Python
-You can load this model with `transformers`:
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-# Example prompt
-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 **Qwen2.5-3B-Instruct**, this fine-tuned model:
-- Provides **more detailed explanations** when answering GSM8K math problems.
-- Improves **logical reasoning** by incorporating DeepSeek-style augmented reasoning.
-- Generates **clearer step-by-step solutions**, making it useful for educational or tutoring applications.
-## Model Directory
-The model is hosted on **Hugging Face Hub**:
-👉 **[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)**
-## License
-This model is released under the **MIT License**, allowing open usage and modifications.
----
-If you have any questions or suggestions for improvements, feel free to reach out!
 🔗 [LinkedIn](https://www.linkedin.com/in/yiqiaoyin/)

+---
+license: mit
+datasets:
+- openai/gsm8k
+- eagle0504/openai-gsm8k-enhanced-using-together-ai-deepseek-train8k-test1k-v1
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+library_name: transformers
+tags:
+- fine-tuned
+- qwen
+- deepseek
+- gsm8k
+- reasoning
+---
+# Qwen 2.5-3B-Instruct Fine-Tuned on OpenAI GSM8K with DeepSeek Augmentation
+## Model Overview
+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.
+### Key Features:
+- **Base Model**: Qwen 2.5-3B-Instruct
+- **Fine-Tuned On**: OpenAI GSM8K dataset
+- **Enhancement**: Answer augmentation with reasoning insights from **DeepSeek-V3-Small**
+- **Improved Reasoning**: Model not only provides correct answers but also **augments** explanations with logical steps inspired by DeepSeek’s generative capabilities.
+## Dataset & Training Details
+- **Dataset**: OpenAI’s GSM8K (Grade School Math 8K), a collection of high-quality math problems designed to test problem-solving skills.
+- **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).
+- **Enhancement**: After fine-tuning on GSM8K, additional reasoning layers were introduced using **DeepSeek-V3-Small**, leading to richer, more interpretable answers.
+- **Training Objective**: Improve step-by-step mathematical reasoning and **enhance logical deductions** in model-generated responses.
+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.
+You will need:
+- Huggingface token
+- Together.AI API Key
+- Unsloth package
+## How to Use Model via Terminal (Mac)
+**Goal** Run Qwen-2.5-3B Instruct on Your Mac Using `llama.cpp`
+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.
+### **Step 1: Install Homebrew (if not installed)**
+Homebrew is required to install `llama.cpp`.
+1. Open **Terminal** (`Cmd + Space`, type `Terminal`, and press **Enter**).
+2. Run:
+   ```sh
+   /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
+   ```
+3. After installation, add Homebrew to your PATH:
+   ```sh
+   echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> ~/.zprofile
+   eval "$(/opt/homebrew/bin/brew shellenv)"
+   ```
+---
+### **Step 2: Install `llama.cpp` via Homebrew**
+Run:
+```sh
+brew install llama.cpp
+```
+Once installed, you should be able to use `llama-cli`.
+---
+### **Step 3: Run Qwen-2.5-3B Instruct with `llama-cli`**
+To run the model, execute:
+```sh
+llama-cli -hf eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3:Q8_0
+```
+---
+### **Step 4: Additional Configurations (If Needed)**
+If you encounter issues or need finer control, you may want to:
+#### **A. Verify Installation**
+Check if `llama-cli` is installed:
+```sh
+llama-cli --version
+```
+If you see a version output, it’s installed correctly.
+#### **B. Run with Explicit Model Path**
+If the default Hugging Face loader doesn't work, you can manually download the model:
+1. **Create a models directory:**
+   ```sh
+   mkdir -p ~/llama_models && cd ~/llama_models
+   ```
+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):
+   ```sh
+   wget https://huggingface.co/eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3/blob/main/unsloth.Q8_0.gguf
+   ```
+3. **Run the model manually**:
+   ```sh
+   llama-cli -m ~/llama_models/Q8_0.gguf
+   ```
+---
+### **Step 5: Test the Model**
+Try prompting it:
+```sh
+llama-cli -m ~/llama_models/Q8_0.gguf -p "Explain quantum computing in simple terms."
+```
+or interactively:
+```sh
+llama-cli -m ~/llama_models/Q8_0.gguf --interactive
+```
+## How to Use Model via Python
+You can load this model with `transformers`:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "eagle0504/qwen-2-5-3b-instruct-using-openai-gsm8k-data-enhanced-with-deepseek-v3"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+# Example prompt
+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 **Qwen2.5-3B-Instruct**, this fine-tuned model:
+- Provides **more detailed explanations** when answering GSM8K math problems.
+- Improves **logical reasoning** by incorporating DeepSeek-style augmented reasoning.
+- Generates **clearer step-by-step solutions**, making it useful for educational or tutoring applications.
+## Model Directory
+The model is hosted on **Hugging Face Hub**:
+👉 **[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)**
+## License
+This model is released under the **MIT License**, allowing open usage and modifications.
+---
+If you have any questions or suggestions for improvements, feel free to reach out!
 🔗 [LinkedIn](https://www.linkedin.com/in/yiqiaoyin/)