Pensez-v0.1-e5
| Property | Value |
|---|---|
| Base Model | Qwen2.5-7B-Instruct |
| Parameters | 7 Billion |
| Training Data | 2,000 samples (1,000 French, 1,000 English) |
| Model URL | HuggingFace: HoangHa/Pensez-v0.1-e5 |
| Author | HoangHa |
What is Pensez-v0.1-e5?
Pensez-v0.1-e5 is a specialized bilingual (French-English) language model designed to maximize reasoning capabilities while using minimal training data. Built upon Qwen 2.5 Instruct 7B, it represents the fifth and final training epoch of the Pensez series, demonstrating superior performance in mathematical and reasoning tasks compared to its base model.
Implementation Details
The model employs several advanced training techniques including Packing Inputs Without Cross-Contamination Attention, Liger Kernel, DeepSpeed 3, and NEFTune Noise for enhanced robustness. Training parameters include a global batch size of 200, learning rate of 1e-5, and cosine scheduler with 5% warmup ratio.
- Maximum sequence length: 16,384 tokens
- Trained over 5 epochs with AdamW optimizer
- Uses special tokens for explicit reasoning guidance
- Implements weight decay of 0.01
Core Capabilities
- Strong performance in French mathematical reasoning (34.58% on Math-hard)
- Robust bilingual understanding across French and English
- High accuracy in boolean question answering (91.57%)
- Enhanced performance in scientific and logical reasoning tasks
- Efficient processing of both simple and complex queries
Frequently Asked Questions
Q: What makes this model unique?
Pensez stands out for its ability to achieve strong reasoning capabilities with a remarkably small training dataset of just 2,000 samples, utilizing specialized tokens and optimized training strategies to maintain performance comparable to larger models.
Q: What are the recommended use cases?
The model excels in mathematical reasoning, scientific problem-solving, and bilingual tasks requiring logical analysis. It's particularly suited for applications needing precise reasoning in both French and English contexts.
