Saluki 27B: A 2-bit Qwen3.8 Model That Excels at Tool Calling
Meet Saluki 27B, a 7.89 GB 2-bit GGUF model based on Qwen3.8-27B under Apache 2.0, outperforming the original model in tool calling tasks.
Stock photo for illustration only, not from the actual event
- Saluki 27B is a 2-bit quantized model derived from Qwen3.8-27B
- Features a compact file size of only 7.89 GB under Apache 2.0
- Outperforms the 54 GB original model specifically in tool calling
- Trades off performance in competition math and logical reasoning
The artificial intelligence landscape has welcomed Saluki 27B, a compact large language model compressed into a 2-bit GGUF format. Boasting a remarkably small file size of just 7.89 GB, this underdog model was built to challenge the capabilities of its much larger parent model, Qwen3.8-27B, which standardly weighs in at 54 GB.
The standout achievement of Saluki 27B lies in its tool-calling capabilities. Despite its heavily reduced footprint, it manages to surpass the original 54 GB model in executing tool-related tasks, proving that aggressive compression does not universally destroy functional competence.
Stock photo for illustration only, not from the actual event
This gain, however, comes with specific trade-offs. Saluki 27B sacrifices ground when it comes to competition-level mathematics and complex general reasoning tasks compared to its uncompressed predecessor.
Extreme model quantization down to 2-bit precision is a major engineering hurdle, as severe compression typically degrades accuracy significantly. The fact that Saluki 27B retains superior tool-calling proficiency while shrinking memory requirements highlights promising pathways for deploying capable AI models on resource-constrained hardware.
Source: MarkTechPost
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