Menda-3B-500
Menda-3B-500 is a fine-tuned version of Qwen/Qwen2.5-3B-Instruct using Guided Reinforcement from Preference Optimization (GRPO). This model represents the 500-step checkpoint from the training process.
Model Details
- Base Model: Qwen/Qwen2.5-3B-Instruct
- Training Method: GRPO (Guided Reinforcement from Preference Optimization)
- Training Steps: 500
- Parameters: 3B
- Context Length: 32K tokens
- Training Data: GSM8K (mathematical reasoning)
Performance
Based on extensive evaluation, the 500-step checkpoint shows strong and balanced performance across multiple benchmarks:
Core Benchmarks (0-shot)
| Benchmark | Score |
|---|---|
| ARC-Challenge | 50.0% |
| BoolQ | 90.0% |
| HellaSwag | 40.0% |
| Lambada | 70.0% |
| PIQA | 90.0% |
| Winogrande | 90.0% |
MMLU Performance
| MMLU Category | Score |
|---|---|
| Overall | 68.60% |
| Humanities | 75.38% |
| Social Sciences | 75.83% |
| STEM | 60.00% |
| Other | 67.69% |
Key Strengths
- Balanced Performance: Maintains strong performance across diverse tasks with minimal trade-offs.
- Improved BoolQ: Achieves 90% on BoolQ, showing excellent reading comprehension capabilities.
- Strong Reasoning: Maintains 90% on both PIQA and Winogrande, demonstrating robust reasoning abilities.
- Efficient Training: Achieves impressive results with relatively minimal training (500 steps).
- Stable Knowledge: Maintains strong MMLU performance (68.60%) across diverse knowledge domains.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "weathermanj/Menda-3B-500"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language models."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
Training Configuration
The model was trained using the GRPO methodology with the following configuration:
- LoRA Rank: 128
- Learning Rate: 5e-6
- Optimizer: AdamW (8-bit)
- Batch Size: 8 per device
- Gradient Accumulation Steps: 4
- Training Samples: 100 examples from GSM8K
License
This model is subject to the license of the original Qwen2.5-3B-Instruct model.