prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF

Lambda-Equulei-1.5B-xLingual

Lambda-Equulei-1.5B-xLingual is a multilingual conversational model fine-tuned from Qwen2-1.5B, specifically designed for cross-lingual chat and experimental conversations across 30+ languages. It brings advanced multilingual understanding and natural dialogue capabilities in a compact size, ideal for international communication tools, language learning platforms, and global conversational assistants.

Model Files

Filename	Size	Format	Description
Lambda-Equulei-1.5B-xLingual.BF16.gguf	3.56 GB	BF16	Brain Float 16-bit quantization
Lambda-Equulei-1.5B-xLingual.F16.gguf	3.56 GB	F16	Half precision (16-bit) floating point
Lambda-Equulei-1.5B-xLingual.F32.gguf	7.11 GB	F32	Full precision (32-bit) floating point
Lambda-Equulei-1.5B-xLingual.Q2_K.gguf	753 MB	Q2_K	2-bit quantization with K-quant
Lambda-Equulei-1.5B-xLingual.Q3_K_L.gguf	980 MB	Q3_K_L	3-bit quantization (Large) with K-quant
Lambda-Equulei-1.5B-xLingual.Q3_K_M.gguf	924 MB	Q3_K_M	3-bit quantization (Medium) with K-quant
Lambda-Equulei-1.5B-xLingual.Q3_K_S.gguf	861 MB	Q3_K_S	3-bit quantization (Small) with K-quant
Lambda-Equulei-1.5B-xLingual.Q4_K_M.gguf	1.12 GB	Q4_K_M	4-bit quantization (Medium) with K-quant
Lambda-Equulei-1.5B-xLingual.Q4_K_S.gguf	1.07 GB	Q4_K_S	4-bit quantization (Small) with K-quant
Lambda-Equulei-1.5B-xLingual.Q5_K_M.gguf	1.29 GB	Q5_K_M	5-bit quantization (Medium) with K-quant
Lambda-Equulei-1.5B-xLingual.Q5_K_S.gguf	1.26 GB	Q5_K_S	5-bit quantization (Small) with K-quant
Lambda-Equulei-1.5B-xLingual.Q6_K.gguf	1.46 GB	Q6_K	6-bit quantization with K-quant
Lambda-Equulei-1.5B-xLingual.Q8_0.gguf	1.89 GB	Q8_0	8-bit quantization

Recommended Usage

• Q4_K_M or Q5_K_M: Best balance of quality and performance for most users
• Q6_K or Q8_0: Higher quality, moderate file sizes
• Q2_K or Q3_K_S: Fastest inference, lower quality (good for resource-constrained environments)
• F16 or BF16: High quality, requires more VRAM
• F32: Highest quality, requires significant VRAM

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):