dphn · text

dolphin-2.2.1-mistral-7b

dphn/dolphin-2.2.1-mistral-7b

dolphin-2.2.1-mistral-7b at Q4_K_M is exactly 4,368,450,304 bytes (4.07 GiB / 4.37 GB) — an effective 4.826 bits per weight, not the nominal 4.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
7.2B
Architecture
llama
32 layers
Context
32,768
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S1.50 GiB1,612,111,4881.781mradermacher
I1-IQ1_M1.63 GiB1,754,455,6801.938mradermacher
I1-IQ2_XXS1.85 GiB1,991,696,0002.200mradermacher
I1-IQ2_XS2.05 GiB2,198,265,4722.428mradermacher
I1-IQ2_S2.15 GiB2,310,930,8802.553mradermacher
I1-IQ2_M2.33 GiB2,500,723,1362.763mradermacher
Q2_K2.53 GiB2,719,251,7123.004QuantFactory
I1-Q2_K2.53 GiB2,719,253,1843.004mradermacher
I1-IQ3_XXS2.63 GiB2,827,354,5603.123mradermacher
I1-IQ3_XS2.81 GiB3,018,827,2643.335mradermacher
Q2_K2.87 GiB3,083,107,2003.406TheBloke
Q3_K_S2.95 GiB3,164,577,4723.496TheBloke
Q3_K_S2.95 GiB3,164,577,8563.496QuantFactory
I1-Q3_K_S2.95 GiB3,164,579,3283.496mradermacher
IQ3_S2.96 GiB3,182,403,6483.516QuantFactory
I1-IQ3_S2.96 GiB3,182,405,1203.516mradermacher
I1-IQ3_M3.06 GiB3,284,903,4243.629mradermacher
Q3_K_M3.28 GiB3,518,996,1603.888TheBloke
Q3_K_M3.28 GiB3,518,996,5443.888QuantFactory
I1-Q3_K_M3.28 GiB3,518,998,0163.888mradermacher
Q3_K_L3.56 GiB3,822,034,6244.222TheBloke
Q3_K_L3.56 GiB3,822,035,0084.222QuantFactory
I1-Q3_K_L3.56 GiB3,822,036,4804.222mradermacher
I1-IQ4_XS3.64 GiB3,907,701,0564.317mradermacher
Q4_03.83 GiB4,108,927,7444.539TheBloke
Q4_03.83 GiB4,108,928,1284.539QuantFactory
I1-Q4_03.84 GiB4,123,609,6644.555mradermacher
Q4_K_S3.86 GiB4,140,385,0244.574TheBloke
Q4_K_S3.86 GiB4,140,385,4084.574QuantFactory
I1-Q4_K_S3.86 GiB4,140,386,8804.574mradermacher
Q4_K_M4.07 GiB4,368,450,3044.826TheBloke
Q4_K_M4.07 GiB4,368,450,6884.826QuantFactory
I1-Q4_K_M4.07 GiB4,368,452,1604.826mradermacher
Q5_K_S4.65 GiB4,997,728,0005.521TheBloke
Q5_04.65 GiB4,997,728,0005.521TheBloke
Q5_K_S4.65 GiB4,997,728,3845.521QuantFactory
Q5_04.65 GiB4,997,728,3845.521QuantFactory
I1-Q5_K_S4.65 GiB4,997,729,8565.521mradermacher
Q5_K_M4.78 GiB5,131,421,4405.669TheBloke
Q5_K_M4.78 GiB5,131,421,8245.669QuantFactory

KV cache by context

unresolved

This model declares a 4,096-token sliding window, but we could not establish which layers use it. Its architecture publishes the layout as a per-layer array inside the model file rather than as a period in config.json, and we have not yet ingested that array.

A flat context × layers × heads figure would be substantially too high, so we are not showing one. This is tracked as a known gap rather than filled with a guess.

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 3.79 GiB. The real file is 4.07 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
32
Attention heads
32
KV heads
8
Head dim
128
Hidden size
4096
Vocab
32,002
Sliding window
4096
SWA period
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does dolphin-2.2.1-mistral-7b need?
Q4_K_M is exactly 4,368,450,304 bytes (4.07 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of dolphin-2.2.1-mistral-7b 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.