CausalLM-7B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 7.72B |
| License | WTFPL |
| Languages | English, Chinese |
| Quantization Options | Q2_K to Q8_0 |
What is CausalLM-7B-GGUF?
CausalLM-7B-GGUF is a powerful language model that represents a significant advancement in efficient AI deployment. Based on the Qwen architecture and utilizing LLaMA2 concepts, this model has been trained on a carefully curated dataset of 1.3B tokens, specifically optimized for speculative sampling.
Implementation Details
The model implements a LLaMA2-style architecture with original MHA attention calculations. It offers multiple quantization options ranging from Q2_K (3.40GB) to Q8_0 (8.21GB), allowing users to balance between model size and performance. The model uses the ChatML format for prompting and requires a non-empty system prompt for optimal performance.
- Impressive benchmark performance: 63.82% on MMLU, 70.27% on CEval
- Multiple quantization options for different hardware capabilities
- Support for both English and Chinese languages
- Optimized for speculative sampling
Core Capabilities
- Advanced text generation and completion
- Strong performance in STEM (56.83%) and Social Sciences (72.41%)
- Efficient deployment through GGUF format
- Multi-language support with strong performance in both English and Chinese
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional performance metrics that rival models up to 33B parameters, despite its smaller 7B parameter size. It achieves this while maintaining efficient deployment options through various quantization levels.
Q: What are the recommended use cases?
The model is particularly well-suited for text generation tasks, educational applications (given its strong STEM and humanities performance), and multilingual applications requiring both English and Chinese language capabilities. It's especially effective when deployed with Q4_K_M quantization for a balance of performance and efficiency.
