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DeepSeek-R1-Distill-Qwen-14B-abliterated

huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated

DeepSeek-R1-Distill-Qwen-14B-abliterated at Q4_K_M is exactly 8,988,110,880 bytes (8.37 GiB / 8.99 GB) — an effective 4.868 bits per weight, not the nominal 4. Its KV cache at 32K is 6.00 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
14.8B
Architecture
qwen2
48 layers
Context
131,072
native (config.json)
License

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S3.36 GiB3,607,994,6561.954mradermacher
I1-IQ1_M3.61 GiB3,872,309,5362.097mradermacher
I1-IQ2_XXS4.02 GiB4,312,834,3362.336mradermacher
I1-IQ2_XS4.38 GiB4,704,575,7762.548mradermacher
I1-IQ2_S4.66 GiB5,003,727,1362.710mradermacher
I1-IQ2_M4.99 GiB5,356,146,9762.901mradermacher
I1-Q2_K_S5.03 GiB5,397,188,8962.923mradermacher
Q2_K5.37 GiB5,770,498,0803.126mradermacher
I1-Q2_K5.37 GiB5,770,498,3363.126mradermacher
I1-IQ3_XXS5.54 GiB5,946,708,2563.221mradermacher
I1-IQ3_XS5.94 GiB6,383,362,3363.458mradermacher
Q3_K_S6.20 GiB6,659,596,3203.607mradermacher
I1-Q3_K_S6.20 GiB6,659,596,5763.607mradermacher
I1-IQ3_S6.23 GiB6,693,019,9363.625mradermacher
I1-IQ3_M6.44 GiB6,916,538,6563.746mradermacher
Q3_K_M6.84 GiB7,339,204,6403.975mradermacher
I1-Q3_K_M6.84 GiB7,339,204,8963.975mradermacher
Q3_K_L7.38 GiB7,924,768,8004.292mradermacher
I1-Q3_K_L7.38 GiB7,924,769,0564.292mradermacher
I1-IQ4_XS7.56 GiB8,119,841,0564.398mradermacher
IQ4_XS7.62 GiB8,186,196,0004.434mradermacher
I1-Q4_07.96 GiB8,544,268,5764.628mradermacher
I1-IQ4_NL7.96 GiB8,549,183,7764.631mradermacher
Q4_K_S7.98 GiB8,573,431,8404.644mradermacher
I1-Q4_K_S7.98 GiB8,573,432,0964.644mradermacher
Q4_K_M8.37 GiB8,988,110,8804.868mradermacher
I1-Q4_K_M8.37 GiB8,988,111,1364.868mradermacher
I1-Q4_18.75 GiB9,392,140,5765.087mradermacher
Q5_K_S9.56 GiB10,266,554,4005.561mradermacher
I1-Q5_K_S9.56 GiB10,266,554,6565.561mradermacher
Q5_K_M9.79 GiB10,508,873,7605.692mradermacher
I1-Q5_K_M9.79 GiB10,508,874,0165.692mradermacher
Q6_K11.29 GiB12,124,684,3206.567mradermacher
I1-Q6_K11.29 GiB12,124,684,5766.567mradermacher
Q8_014.62 GiB15,701,598,2408.505mradermacher

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.75 GiB0.75 GiB48 / 0 / 0
8,1921.50 GiB1.50 GiB48 / 0 / 0
16,3843.00 GiB3.00 GiB48 / 0 / 0
32,7686.00 GiB6.00 GiB48 / 0 / 0
65,53612.00 GiB12.00 GiB48 / 0 / 0
131,07224.00 GiB24.00 GiB48 / 0 / 0

Compare with

same modality, comparable size

Will it run on your card?

full quant x context sweep

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 7.74 GiB. The real file is 8.37 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
48
Attention heads
40
KV heads
8
Head dim
128
Hidden size
5120
Vocab
152,064
Sliding window
131072
SWA period
MLA
no
Experts
Experts per token
use_sliding_window
false

This model declares a sliding window but sets use_sliding_window: false, so the window is not applied. Honouring the field without the flag understates KV for the whole family.

Questions people ask

How much VRAM does DeepSeek-R1-Distill-Qwen-14B-abliterated need?
Q4_K_M is exactly 8,988,110,880 bytes (8.37 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is DeepSeek-R1-Distill-Qwen-14B-abliterated's KV cache?
6.00 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.
Which quantization of DeepSeek-R1-Distill-Qwen-14B-abliterated should I use?
Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.