codelion
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gemma-3-1b-it-reasoning-grpo-lora

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 ---
+base_model: google/gemma-3-1b-it
+tags:
+- ellora
+- lora
+- reasoning
+- chain-of-thought
+- grpo
+- thinking
+- preference-learning
+- self-improvement
+- peft
+- gemma
+library_name: peft
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+inference: true
+model_type: gemma
 ---
+# codelion/gemma-3-1b-it-reasoning-grpo-lora
+## 🧠 Reasoning LoRA with GRPO Training
+This LoRA adapter enhances google/gemma-3-1b-it with structured reasoning capabilities using `<think></think>` tags. Trained with GRPO (Group Relative Policy Optimization) on self-generated preference data.
+## 🎯 Key Features
+- **Structured Thinking**: Teaches models to use `<think></think>` tags for chain-of-thought reasoning
+- **GRPO Training**: Uses preference learning to optimize reasoning quality
+- **Self-Generated Data**: No external datasets required - uses Magpie approach
+- **Multi-Domain**: Effective across mathematics, logic, science, and problem-solving
+## 📊 Performance Metrics
+- **Base Model**: google/gemma-3-1b-it
+- **Training Method**: GRPO (Group Relative Policy Optimization)
+- **LoRA Rank**: 64
+- **LoRA Alpha**: 128
+- **Training Samples**: 614
+- **Thinking Tag Usage**: 0.0%
+- **Average Quality Score**: 0.00
+## 🔧 Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# Load base model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-3-1b-it",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-1b-it")
+# Load reasoning LoRA adapter
+model = PeftModel.from_pretrained(model, "codelion/gemma-3-1b-it-reasoning-grpo-lora")
+# Use with thinking prompt
+prompt = '''Think step by step and use <think></think> tags to show your reasoning process.
+Problem: If a train travels 120 miles in 2 hours, then increases its speed by 30 mph for the next hour, how many total miles does it travel?
+Response:'''
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.5)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+## 📈 Expected Output Format
+The model will generate responses with structured thinking:
+```
+<think>
+First, I need to find the train's initial speed.
+Speed = Distance / Time = 120 miles / 2 hours = 60 mph
+For the first 2 hours: 120 miles
+For the next hour, speed increases by 30 mph: 60 + 30 = 90 mph
+Distance in third hour: 90 mph × 1 hour = 90 miles
+Total distance = 120 + 90 = 210 miles
+</think>
+To solve this step by step:
+First, I'll find the train's initial speed:
+- Distance = 120 miles, Time = 2 hours
+- Speed = 120 ÷ 2 = 60 mph
+Next, I'll calculate the distance for each segment:
+- First 2 hours: 120 miles (given)
+- Third hour: speed increases by 30 mph → 60 + 30 = 90 mph
+- Distance in third hour: 90 × 1 = 90 miles
+Total distance = 120 + 90 = 210 miles
+```
+## 🧪 Training Details
+- **Method**: GRPO (Group Relative Policy Optimization)
+- **Data Generation**: Magpie approach with reasoning-focused prompts
+- **Preference Learning**: Multiple responses ranked by reasoning quality
+- **Domains**: Mathematics, logic puzzles, science, programming, philosophy
+- **Quality Scoring**: Based on thinking tag usage, reasoning markers, and structure
+## 📚 Training Data
+The model was trained on self-generated reasoning problems across multiple domains:
+- Mathematical problem-solving
+- Logic puzzles and riddles
+- Scientific analysis
+- Programming challenges
+- Philosophical reasoning
+- Decision-making scenarios
+## 🎭 Reasoning Patterns Learned
+- **Step-by-step analysis**: Breaking complex problems into smaller parts
+- **Causal reasoning**: Using "because", "therefore", "since" connections
+- **Sequential thinking**: "First", "next", "then", "finally" progression
+- **Structured output**: Clear separation of thinking and final response
+## 🔬 Evaluation
+The adapter was evaluated on diverse reasoning tasks:
+- Thinking tag usage rate: 0.0%
+- Average reasoning quality score: 0.00
+- Response comprehensiveness: 0 words average
+## 🏷️ Related
+- **Dataset**: [codelion/gemma-3-1b-it-magpie-reasoning](https://huggingface.co/datasets/codelion/gemma-3-1b-it-magpie-reasoning)
+- **Base Model**: [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it)
+- **Framework**: [PEFT](https://github.com/huggingface/peft)
+- **Training Method**: GRPO (Group Relative Policy Optimization)
+---
+*This adapter is part of the [Ellora project](https://github.com/codelion/ellora) - standardized recipes for enhancing LLM capabilities.*