33 quants from full-precision F16/BF16 down to 1-bit and ternary, including imatrix-guided IQ quants for best quality-per-byte at the low end.

Nanbeige4.2-3B support is merged into mainline llama.cpp — just build the latest:

bash

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp && cmake -B build && cmake --build build -j

🚀 Run

llama.cpp (chat):

./build/bin/llama-cli -hf iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M -cnv

or with a local file:

./build/bin/llama-cli -m Nanbeige4.2-3B-Q4_K_M.gguf -cnv

llama-server (OpenAI-compatible API):

./build/bin/llama-server -m Nanbeige4.2-3B-Q4_K_M.gguf -c 8192

Download a single quant:

bash

pip install -U "huggingface_hub[cli]"
hf download iamimmanuelraj/Nanbeige4.2-3B-GGUF Nanbeige4.2-3B-Q4_K_M.gguf --local-dir .

📦 Quants

⭐ = recommended. IQ quants are imatrix-guided (better quality at the same size).

Full precision

Quant	Size	Notes
F16	8.34 GB	Full 16-bit reference
BF16	8.34 GB	Full 16-bit (bfloat)

High quality

Quant	Size	Notes
⭐ Q8_0	4.43 GB	Near-lossless, best quality
Q6_K	3.42 GB	Excellent, hard to distinguish from F16
⭐ Q5_K_M	2.99 GB	Very high quality, great default for quality
Q5_K_S	2.94 GB	Very high quality, slightly smaller
Q5_0	2.94 GB	Legacy 5-bit
Q5_1	3.17 GB	Legacy 5-bit, larger

Balanced (recommended range)

Quant	Size	Notes
⭐ Q4_K_M	2.57 GB	Best size/quality balance — start here
Q4_K_S	2.50 GB	Balanced, slightly smaller
Q4_0	2.48 GB	Legacy 4-bit
Q4_1	2.71 GB	Legacy 4-bit, larger
⭐ IQ4_XS	2.38 GB	Imatrix 4-bit, beats Q4 at smaller size
IQ4_NL	2.49 GB	Imatrix 4-bit non-linear

Small

Quant	Size	Notes
Q3_K_L	2.31 GB	3-bit, larger/better
Q3_K_M	2.17 GB	3-bit balanced
Q3_K_S	2.00 GB	3-bit smaller
IQ3_M	2.08 GB	Imatrix 3-bit, good
IQ3_S	2.00 GB	Imatrix 3-bit
IQ3_XS	1.93 GB	Imatrix 3-bit smaller
IQ3_XXS	1.78 GB	Imatrix 3-bit, very small

Very small

Quant	Size	Notes
Q2_K	1.76 GB	2-bit, usable
Q2_K_S	1.68 GB	2-bit smaller
Q2_0	1.60 GB	Legacy 2-bit
IQ2_M	1.66 GB	Imatrix 2-bit, best small quality
IQ2_S	1.59 GB	Imatrix 2-bit
IQ2_XS	1.46 GB	Imatrix 2-bit smaller
IQ2_XXS	1.37 GB	Imatrix 2-bit, very small

Extreme / experimental

Quant	Size	Notes
IQ1_M	1.27 GB	Imatrix 1-bit, experimental
IQ1_S	1.21 GB	Imatrix 1-bit, most degraded
Q1_0	0.94 GB	1-bit, smallest, heavy quality loss
TQ1_0	1.37 GB	Ternary, experimental
TQ2_0	1.52 GB	Ternary, experimental

Which should I pick?

Most people → Q4_K_M (2.57 GB): best balance of size, speed, quality.
Want max quality → Q8_0 or Q6_K.
Tight on RAM/VRAM → IQ4_XS (2.38 GB) or Q3_K_M (2.17 GB).
Tiny devices → IQ2_M (1.66 GB); below that quality drops sharply.
≤ 1.3 GB (IQ1/Q1/TQ): experimental — expect noticeable degradation.

Prefer IQ over the same-size legacy quant when available — imatrix calibration gives better quality per byte.

Notes

IQ quants built with an importance matrix (imatrix) for better low-bit fidelity.
Q8_0 KV cache recommended for this architecture.
License follows the base model's license.

Quantized with llama.cpp.