huihui-ai · text

DeepSeek-R1-Distill-Qwen-7B-abliterated-v2

huihui-ai/DeepSeek-R1-Distill-Qwen-7B-abliterated-v2

DeepSeek-R1-Distill-Qwen-7B-abliterated-v2 at Q4_K_M is exactly 4,683,073,952 bytes (4.36 GiB / 4.68 GB) — an effective 4.919 bits per weight, not the nominal 4. Its KV cache at 32K is 1.75 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
7.6B
Architecture
qwen2
28 layers
Context
131,072
native (config.json)
License

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S1.77 GiB1,903,668,0962.000mradermacher
I1-IQ1_M1.90 GiB2,042,196,8642.145mradermacher
I1-IQ2_XXS2.12 GiB2,273,078,1442.388mradermacher
I1-IQ2_XS2.30 GiB2,469,022,5922.594mradermacher
I1-IQ2_S2.42 GiB2,595,638,1442.727mradermacher
I1-IQ2_M2.59 GiB2,780,343,1682.921mradermacher
Q2_K_S2.64 GiB2,834,074,0162.977Melvin56
I1-Q2_K_S2.64 GiB2,834,074,4962.977mradermacher
Q2_K2.81 GiB3,015,940,5123.168Melvin56
Q2_K2.81 GiB3,015,940,7363.168mradermacher
I1-Q2_K2.81 GiB3,015,940,9923.168mradermacher
I1-IQ3_XXS2.90 GiB3,114,515,3283.272mradermacher
I1-IQ3_XS3.12 GiB3,346,256,7683.515mradermacher
Q3_K_S3.25 GiB3,492,369,0243.669mradermacher
I1-Q3_K_S3.25 GiB3,492,369,2803.669mradermacher
I1-IQ3_S3.26 GiB3,499,193,2163.676mradermacher
I1-IQ3_M3.33 GiB3,574,012,8003.754mradermacher
Q3_K_M3.55 GiB3,808,391,5844.001Melvin56
Q3_K_M3.55 GiB3,808,391,8084.001mradermacher
I1-Q3_K_M3.55 GiB3,808,392,0644.001mradermacher
Q3_K_L3.81 GiB4,088,459,9044.295mradermacher
I1-Q3_K_L3.81 GiB4,088,460,1604.295mradermacher
I1-IQ4_XS3.93 GiB4,218,473,3444.431mradermacher
IQ4_XS3.96 GiB4,250,299,0084.465mradermacher
I1-IQ4_NL4.13 GiB4,437,814,1444.662mradermacher
Q4_04.14 GiB4,444,121,5044.668Melvin56
I1-Q4_04.14 GiB4,444,121,9844.668mradermacher
Q4_K_S4.15 GiB4,457,769,6004.683mradermacher
I1-Q4_K_S4.15 GiB4,457,769,8564.683mradermacher
Q4_K_M4.36 GiB4,683,073,9524.919Melvin56
Q4_K_M4.36 GiB4,683,074,1764.919mradermacher
I1-Q4_K_M4.36 GiB4,683,074,4324.919mradermacher
I1-Q4_14.54 GiB4,873,284,4805.119mradermacher
Q5_K_S4.95 GiB5,315,177,0885.583mradermacher
I1-Q5_K_S4.95 GiB5,315,177,3445.583mradermacher
Q5_K_M5.07 GiB5,444,831,6485.720Melvin56
Q5_K_M5.07 GiB5,444,831,8725.720mradermacher
I1-Q5_K_M5.07 GiB5,444,832,1285.720mradermacher
Q6_K5.82 GiB6,254,199,2006.570Melvin56
Q6_K5.82 GiB6,254,199,4246.570mradermacher

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.22 GiB0.22 GiB28 / 0 / 0
8,1920.44 GiB0.44 GiB28 / 0 / 0
16,3840.88 GiB0.88 GiB28 / 0 / 0
32,7681.75 GiB1.75 GiB28 / 0 / 0
65,5363.50 GiB3.50 GiB28 / 0 / 0
131,0727.00 GiB7.00 GiB28 / 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 3.99 GiB. The real file is 4.36 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
28
Attention heads
28
KV heads
4
Head dim
128
Hidden size
3584
Vocab
152,064
Sliding window
4096
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-7B-abliterated-v2 need?
Q4_K_M is exactly 4,683,073,952 bytes (4.36 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-7B-abliterated-v2's KV cache?
1.75 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-7B-abliterated-v2 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.