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liodon-ai/DeepHat-V1-7B-imatrix-GGUF

sourceHugging Faceotherupdated 3mo agoView on Hugging Face

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Model Card

DeepHat-V1-7B — iMatrix GGUF

GGUF quantizations of DeepHat/DeepHat-V1-7B, published by Liodon AI.

Quick Start

llama.cpp

bash

llama-cli -hf liodon-ai/DeepHat-V1-7B-imatrix-GGUF:Q4_K_M

Ollama

bash

ollama run hf.co/liodon-ai/DeepHat-V1-7B-imatrix-GGUF:Q4_K_M

LM Studio / Jan — search liodon-ai/DeepHat-V1-7B-imatrix-GGUF and pick your quant.

Quants

Quant	Size	VRAM est.	Notes
`IQ2_M`	2.78 GB	~3 GB	2-bit, iMatrix — smallest usable
`IQ3_M`	3.57 GB	~4 GB	3-bit, iMatrix — great quality/size tradeoff
`IQ4_XS`	4.22 GB	~5 GB	4-bit extra-small, iMatrix
`Q4_K_M`	4.68 GB	~5 GB	4-bit, iMatrix-calibrated (recommended)
`Q5_K_M`	5.44 GB	~6 GB	5-bit, iMatrix-calibrated
`Q6_K`	6.25 GB	~7 GB	6-bit, iMatrix-calibrated, near-lossless
`Q8_0`	8.10 GB	~9 GB	8-bit, essentially lossless

What is iMatrix?

Standard quantization treats all weights equally. iMatrix runs 128 calibration chunks through the full-precision model to find which weights matter most, then allocates more precision where it counts. At Q2/Q3/Q4 this means noticeably better coherence and instruction-following — same file size, better output.

Calibration: 2M tokens of WikiText-103.

Also see plain (non-iMatrix) quants: liodon-ai/DeepHat-V1-7B-GGUF

Source

—Model: DeepHat/DeepHat-V1-7B
—License: other

Quantized by [Liodon AI](https://huggingface.co/liodon-ai)