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Model: RuudFontys/socratic-tutor-qwen2.5 Source: Original Platform
2026-05-13 20:57:23 +08:00
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+---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+- qwen2.5
+- fine-tuned
+- socratic-method
+- education
+- tutoring
+- dialogue
+- peft
+- lora
+library_name: transformers
+pipeline_tag: text-generation
+model_type: qwen2
+datasets:
+- synthetic
+metrics:
+- perplexity
+widget:
+- example_title: "Learning Question"
+  text: |
+    <|im_start|>system
+    You are a Socratic tutor who guides learning through questioning. Your role is to help students discover insights themselves by asking probing questions rather than providing direct answers.<|im_end|>
+    <|im_start|>user
+    Can you explain photosynthesis to me?<|im_end|>
+    <|im_start|>assistant
+inference:
+  parameters:
+    max_new_tokens: 256
+    temperature: 0.7
+    do_sample: true
+    top_p: 0.9
+---
+
+# 🎭 Socratic Tutor - Qwen2.5 Fine-tuned
+
+A fine-tuned Qwen2.5-7B model designed to act as a Socratic tutor, guiding learning through questioning rather than providing direct answers.
+
+## Model Description
+
+This model has been fine-tuned on synthetic Socratic dialogue data to embody the teaching philosophy of Socrates - helping students discover insights themselves through probing questions and guided inquiry.
+
+### Key Features
+- **Socratic Method**: Asks thought-provoking questions to guide learning
+- **Educational Focus**: Designed for tutoring and educational conversations
+- **Based on Qwen2.5-7B**: Built on Alibaba's powerful instruction-following model
+- **LoRA Fine-tuning**: Efficiently trained using Low-Rank Adaptation
+
+## Training Details
+
+- **Base Model**: Qwen/Qwen2.5-7B-Instruct
+- **Training Method**: LoRA (Low-Rank Adaptation)
+- **Training Data**: 350 synthetic Socratic dialogues
+- **Training Duration**: 3 epochs
+- **Final Loss**: 1.16 (down from 4.86)
+
+### Training Configuration
+- LoRA rank: 16
+- Learning rate: 0.0002
+- Batch size: 16 (effective)
+- Max sequence length: 2048 tokens
+
+## Available Formats
+
+### HuggingFace Format (15.2GB)
+The full precision model in standard HuggingFace format.
+
+### GGUF Format (4.4GB)
+**File**: `socratic-tutor-v2-q4_k_m.gguf`
+- **Quantization**: Q4_K_M (4-bit mixed quantization)
+- **Size**: 4.4GB (down from 15GB)
+- **Quality**: Excellent balance of size and performance
+- **Compatible with**: llama.cpp, Ollama, and other GGUF-compatible tools
+
+> **Important**: The GGUF file contains the default Qwen2.5 chat template. To activate Socratic tutoring behavior, you must provide the custom system prompt shown below when using the model.
+
+## Usage
+
+### HuggingFace Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("RuudFontys/socratic-tutor-qwen2.5")
+tokenizer = AutoTokenizer.from_pretrained("RuudFontys/socratic-tutor-qwen2.5")
+
+# Example conversation
+messages = [
+    {"role": "system", "content": "You are a Socratic tutor who guides learning through questioning. Your role is to help students discover insights themselves by asking probing questions rather than providing direct answers."},
+    {"role": "user", "content": "Can you explain photosynthesis to me?"}
+]
+
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
+outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### GGUF with llama.cpp
+
+```bash
+# Download the GGUF file
+wget https://huggingface.co/RuudFontys/socratic-tutor-qwen2.5/resolve/main/socratic-tutor-v2-q4_k_m.gguf
+
+# Run with llama.cpp
+./llama-cli -m socratic-tutor-v2-q4_k_m.gguf \
+  --system-prompt "You are a Socratic tutor who guides learning through questioning. Your role is to help students discover insights themselves by asking probing questions rather than providing direct answers." \
+  --chat
+```
+
+### Recommended System Prompt
+
+For best results, use this system prompt:
+
+```
+You are a Socratic tutor who guides learning through questioning. Your role is to help students discover insights themselves by asking probing questions rather than providing direct answers.
+
+Key principles:
+- Ask thought-provoking questions that lead students to discover answers themselves
+- Avoid giving direct explanations unless absolutely necessary
+- Build on student responses with follow-up questions
+- Help students think critically and make connections
+- Guide them through reasoning processes step by step
+- Encourage curiosity and deeper exploration of topics
+
+Remember: The goal is not to show how much you know, but to help the student learn through their own discovery.
+```
+
+## Example Interactions
+
+**Student**: "Why is my boxplot showing outliers?"
+**Tutor**: "Outliers are beyond 1.5×IQR from the quartiles. Which values exceed that threshold in your data?"
+
+**Student**: "How does machine learning work?"
+**Tutor**: "What do you think it means for a machine to 'learn' from data? How might that differ from how humans learn?"
+
+## Intended Use
+
+This model is designed for:
+- Educational tutoring and guidance
+- Socratic dialogue practice
+- Critical thinking development
+- Question-based learning approaches
+
+## Limitations
+
+- May not provide direct answers when they would be more appropriate
+- Performance depends on the quality of input questions
+- Limited to the knowledge in the base Qwen2.5 model
+
+## Citation
+
+If you use this model, please cite:
+```
+@misc{socratic-tutor-qwen25,
+  title={Socratic Tutor: A Fine-tuned Qwen2.5 Model for Educational Dialogue},
+  author={RuudFontys},
+  year={2025},
+  url={https://huggingface.co/RuudFontys/socratic-tutor-qwen2.5}
+}
+```
+
+## License
+
+This model inherits the license from the base Qwen2.5 model. Please refer to the original model's licensing terms.