Pensez-v0.1-e5-GGUF
| Property | Value |
|---|---|
| Base Model | Qwen 2.5 Instruct 7B |
| License | Apache 2.0 |
| Author | HoangHa |
| Training Data | 2,000 samples (1,000 French, 1,000 English) |
What is Pensez-v0.1-e5-GGUF?
Pensez is a specialized bilingual (French-English) language model designed to enhance reasoning capabilities while minimizing training data requirements. Built on Qwen 2.5 Instruct 7B, this model represents the fifth epoch of training and has been converted to the GGUF format for improved compatibility and deployment.
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 0.05 warmup ratio.
- Maximum sequence length: 16,384 tokens
- Optimizer: AdamW with 0.01 weight decay
- Training duration: 5 epochs on curated dataset
- Special reasoning tokens:
... for explicit reasoning guidance
Core Capabilities
- Superior performance in French mathematical reasoning tasks (34.58% accuracy on Math-hard)
- Strong bilingual reasoning abilities in both French and English
- Efficient handling of both simple and complex reasoning tasks
- High accuracy on boolean question answering (91.57% on BoolQA)
Frequently Asked Questions
Q: What makes this model unique?
Pensez stands out for its ability to achieve strong reasoning capabilities with minimal training data (just 2,000 samples) and its specialized approach to handling different complexity levels of reasoning tasks. The model employs explicit reasoning tokens and shows particular strength in French-language mathematical and scientific reasoning.
Q: What are the recommended use cases?
The model is particularly well-suited for: French-language mathematical problem solving, bilingual reasoning tasks, scientific question answering, and general-purpose French-English language understanding. It's especially effective when explicit reasoning steps are required for complex problem-solving.
