Yi-34B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 34.4B |
| Model Type | Yi Architecture |
| License | Yi License |
| Author | 01-ai (Original) / TheBloke (GGUF) |
What is Yi-34B-GGUF?
Yi-34B-GGUF is a quantized version of the powerful Yi-34B language model, optimized for efficient CPU and GPU inference. Created by TheBloke, this model offers multiple quantization options ranging from 2-bit to 8-bit precision, allowing users to balance performance and resource requirements. The model demonstrates exceptional performance across various benchmarks, including MMLU (76.3%), CMMLU (83.7%), and C-Eval (81.4%).
Implementation Details
The model is available in multiple GGUF formats, each optimized for different use cases. The recommended Q4_K_M variant offers a balanced approach between model size (20.66 GB) and quality preservation. The model supports context lengths up to 4K tokens by default, extensible to 32K during inference.
- Multiple quantization options from Q2_K to Q8_0
- GPU layer offloading support
- Compatible with llama.cpp and various UI implementations
- Optimized for both English and Chinese language tasks
Core Capabilities
- Strong performance in common sense reasoning and reading comprehension
- Excellent multilingual capabilities (English/Chinese)
- Flexible deployment options from consumer hardware to server environments
- Extended context length support up to 32K tokens
Frequently Asked Questions
Q: What makes this model unique?
Yi-34B-GGUF stands out for its exceptional balance of performance and efficiency, offering state-of-the-art results across multiple benchmarks while providing various quantization options for different hardware configurations. Its bilingual capabilities and extensive context length support make it particularly versatile.
Q: What are the recommended use cases?
The model is well-suited for a wide range of applications including text generation, analysis, and completion tasks. For most users, the Q4_K_M quantization offers the best balance of quality and resource usage, while those with limited hardware can opt for lighter versions like Q3_K_S.
