manaca-1b-base-GGUF

NANI-Nithin/manaca-1b-base-GGUF

manaca-1b-base-GGUF is a text generation model from NANI-Nithin. Use it when you need the model to write or continue text. It is set up for gguf.

GGUF quantizations of menezesbruno/manaca-1b-base, covering 30 files (27.0 GB total).

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.gguf29 GB · 100%

At a glance

Task: Text Generation
Library: gguf
Access: Public
Created: Sep 5, 2026
Updated: Sep 7, 2026
SHA: 68f8e8dd

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Base models

Model card

From the Hugging Face model README

manaca-1b-base GGUF

GGUF quantizations of menezesbruno/manaca-1b-base, covering 30 files (27.0 GB total).

Files

File	Quant	Size	Notes
manaca-1b-base-BF16.gguf	`BF16`	3.21 GB	Full precision source. Every quant below is cut from this file.
manaca-1b-base-Q8_0.gguf	`Q8_0`	1.71 GB	Effectively lossless. Use when disk and RAM are not the constraint.
manaca-1b-base-Q6_K.gguf	`Q6_K`	1.32 GB	Near-lossless; the last stop before quality becomes measurable.
manaca-1b-base-Q5_K_M.gguf	`Q5_K_M`	1.15 GB	Very good quality, noticeably smaller than Q6_K.
manaca-1b-base-Q5_K_S.gguf	`Q5_K_S`	1.12 GB	Slightly smaller than Q5_K_M for a slight quality cost.
manaca-1b-base-Q5_1.gguf	`Q5_1`	1.22 GB	Legacy. Prefer Q5_K_M.
manaca-1b-base-Q5_0.gguf	`Q5_0`	1.12 GB	Legacy. Prefer Q5_K_M.
manaca-1b-base-Q4_K_M.gguf	`Q4_K_M`	0.99 GB	The usual default. Best quality-per-byte for most people.
manaca-1b-base-Q4_K_S.gguf	`Q4_K_S`	0.94 GB	A little smaller than Q4_K_M, a little worse.
manaca-1b-base-IQ4_NL.gguf	`IQ4_NL`	0.94 GB	Non-linear 4-bit; good on hardware without fast K-quant kernels.
manaca-1b-base-IQ4_XS.gguf	`IQ4_XS`	0.90 GB	Best sub-4.5bpw option; usually beats Q4_K_S at a smaller size.
manaca-1b-base-Q4_1.gguf	`Q4_1`	1.03 GB	Legacy. Prefer Q4_K_M.
manaca-1b-base-Q4_0.gguf	`Q4_0`	0.94 GB	Legacy round-to-nearest. Prefer Q4_K_M unless a runtime needs this.
manaca-1b-base-Q3_K_L.gguf	`Q3_K_L`	0.87 GB	Small, with real quality loss. Usable when RAM is tight.
manaca-1b-base-Q3_K_M.gguf	`Q3_K_M`	0.81 GB	Smaller again; noticeable degradation.
manaca-1b-base-IQ3_M.gguf	`IQ3_M`	0.76 GB	Strong at ~3.7bpw, clearly better than Q3_K_M.
manaca-1b-base-IQ3_S.gguf	`IQ3_S`	0.74 GB	Slightly smaller than IQ3_M.
manaca-1b-base-Q3_K_S.gguf	`Q3_K_S`	0.74 GB	Aggressive. Prefer IQ3_M at a similar size.
manaca-1b-base-IQ3_XS.gguf	`IQ3_XS`	0.71 GB	Aggressive but coherent.
manaca-1b-base-IQ3_XXS.gguf	`IQ3_XXS`	0.66 GB	Very aggressive; the last coherent step down.
manaca-1b-base-Q2_K.gguf	`Q2_K`	0.64 GB	Very small, heavily degraded. For experimentation.
manaca-1b-base-IQ2_M.gguf	`IQ2_M`	0.60 GB	The smallest size most people find usable.
manaca-1b-base-Q2_K_S.gguf	`Q2_K_S`	0.61 GB	Smaller than Q2_K, at a further quality cost.
manaca-1b-base-IQ2_S.gguf	`IQ2_S`	0.56 GB	Below the usual usability line.
manaca-1b-base-IQ2_XS.gguf	`IQ2_XS`	0.53 GB	Experimental.
manaca-1b-base-IQ2_XXS.gguf	`IQ2_XXS`	0.49 GB	Experimental.
manaca-1b-base-Q2_0.gguf	`Q2_0`	0.56 GB	Extreme, group-64. Included for completeness.
manaca-1b-base-IQ1_M.gguf	`IQ1_M`	0.44 GB	Extreme. Expect substantial degradation.
manaca-1b-base-IQ1_S.gguf	`IQ1_S`	0.41 GB	Extreme. Expect substantial degradation.
manaca-1b-base-Q1_0.gguf	`Q1_0`	0.31 GB	Extreme. Included for completeness.

Which one should I download?

Pick the largest file that leaves a couple of gigabytes of headroom on the device you will run it on — the model has to fit in RAM (or VRAM, if you are offloading) alongside the KV cache and the OS.

Plenty of memory: Q6_K or Q8_0.
The usual choice: Q4_K_M.
Tight on memory: IQ4_XS, then IQ3_M, then IQ2_M.
The IQ* files generally beat a Q* file of similar size, at the cost of slightly slower inference on some hardware.

Usage

llama-cli -hf NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M -p "Hello"

Or download one file and point at it directly:

huggingface-cli download NANI-Nithin/manaca-1b-base-GGUF manaca-1b-base-Q4_K_M.gguf --local-dir .
llama-cli -m manaca-1b-base-Q4_K_M.gguf -p "Hello"

Quantized with llama.cpp by AgentQuantix on 2026-09-07. Licensing follows the base model.

Task: Text Generation
Library: gguf
Created: Sep 5, 2026
Updated: Sep 7, 2026