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Qwen2.5-VL-7B-Instruct-heretic

coder3101/Qwen2.5-VL-7B-Instruct-heretic

Qwen2.5-VL-7B-Instruct-heretic at Q4_K_M is exactly 4,683,073,088 bytes (4.36 GiB / 4.68 GB) — an effective 4.518 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
8.3B
Architecture
qwen2vl
28 layers
Context
128,000
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S1.77 GiB1,903,667,0401.837mradermacher
I1-IQ1_M1.90 GiB2,042,195,8081.970mradermacher
I1-IQ2_XXS2.12 GiB2,273,077,0882.193mradermacher
I1-IQ2_XS2.30 GiB2,469,021,5362.382mradermacher
I1-IQ2_S2.42 GiB2,595,637,0882.504mradermacher
I1-IQ2_M2.59 GiB2,780,342,1122.682mradermacher
I1-Q2_K_S2.64 GiB2,834,073,4402.734mradermacher
Q2_K2.81 GiB3,015,939,6482.910mradermacher
I1-Q2_K2.81 GiB3,015,939,9362.910mradermacher
I1-IQ3_XXS2.90 GiB3,114,514,2723.005mradermacher
I1-IQ3_XS3.12 GiB3,346,255,7123.228mradermacher
Q3_K_S3.25 GiB3,492,367,9363.369mradermacher
I1-Q3_K_S3.25 GiB3,492,368,2243.369mradermacher
I1-IQ3_S3.26 GiB3,499,192,1603.376mradermacher
I1-IQ3_M3.33 GiB3,574,011,7443.448mradermacher
Q3_K_M3.55 GiB3,808,390,7203.674mradermacher
I1-Q3_K_M3.55 GiB3,808,391,0083.674mradermacher
Q3_K_L3.81 GiB4,088,458,8163.944mradermacher
I1-Q3_K_L3.81 GiB4,088,459,1043.944mradermacher
I1-IQ4_XS3.93 GiB4,218,472,2884.070mradermacher
IQ4_XS3.96 GiB4,250,297,9204.101mradermacher
I1-IQ4_NL4.13 GiB4,437,813,0884.282mradermacher
I1-Q4_04.14 GiB4,444,120,9284.287mradermacher
Q4_K_S4.15 GiB4,457,768,5124.301mradermacher
I1-Q4_K_S4.15 GiB4,457,768,8004.301mradermacher
Q4_K_M4.36 GiB4,683,073,0884.518mradermacher
I1-Q4_K_M4.36 GiB4,683,073,3764.518mradermacher
I1-Q4_14.54 GiB4,873,283,4244.702mradermacher
Q5_K_S4.95 GiB5,315,176,0005.128mradermacher
I1-Q5_K_S4.95 GiB5,315,176,2885.128mradermacher
Q5_K_M5.07 GiB5,444,830,7845.253mradermacher
I1-Q5_K_M5.07 GiB5,444,831,0725.253mradermacher
Q6_K5.82 GiB6,254,198,3366.034mradermacher
I1-Q6_K5.82 GiB6,254,198,6246.034mradermacher
Q8_07.54 GiB8,098,524,7367.813mradermacher
F1614.19 GiB15,237,852,73614.701mradermacher

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 4.34 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
32768
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 Qwen2.5-VL-7B-Instruct-heretic need?
Q4_K_M is exactly 4,683,073,088 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 Qwen2.5-VL-7B-Instruct-heretic'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 Qwen2.5-VL-7B-Instruct-heretic 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.