Models / florence2

Florence-2-large

Reconstructed from its own config.json with no weights read. 618K downloads on Hugging Face.

Our count against the checkpoint

The left number comes from the graph. The right one is the number of scalars in the published weight files. Nothing on this page was tuned to make them agree.

Derived from structure

208M

207,844,352 parameters

In the published checkpoint

777M

776,721,497 scalars · safetensors.total, read 2025-08-04

Delta

-73.2%

custom-code This repository ships its own modeling code (`auto_map`, e.g. `configuration_florence2.py`), so `config.json` names a class in the repo rather than an architecture `transformers` defines. The graph below is what those config keys mean under `transformers` semantics, which is not necessarily what the repo's own file builds. A gap here is a statement about what we read, not about the checkpoint.

What it costs to run

Cost is a roofline estimate on the priced GPU for 10 epochs at batch 32 over 50,000 samples (assumed; no dataset attached). GPU fit is fp32 weights plus gradients plus two Adam moments (16 bytes per parameter) with 1.3x headroom; activations are not included and grow with batch size.

Layers

Will it forward-pass

Yes

Priced on

A10G (24GB)

Est. one run

$30.91

Card	Memory
T4 (16GB)	weights + activations	fits
A100 (40GB)	weights + activations	fits
H100 (80GB)	weights + activations	fits

Structure

52 nodes. Output shapes are propagated from the input shape, batch dimension excluded.

	Layer	Type	Output shape
1	Input	Input	1 × 4096
2	Embedding	Embedding	1 × 4096 × 1024
3	Positional_Embedding	Learned Pos Embed	1 × 4096 × 1024
4	Attention_1	Multi-Head Attention	1 × 4096 × 1024
5	Add_1	Add	1 × 4096 × 1024
6	LayerNorm_1_1	LayerNorm	1 × 4096 × 1024
7	FFN_1	Feed Forward	1 × 4096 × 1024
8	Attention_2	Multi-Head Attention	1 × 4096 × 1024
9	Add_2	Add	1 × 4096 × 1024
10	LayerNorm_2_1	LayerNorm	1 × 4096 × 1024
11	FFN_2	Feed Forward	1 × 4096 × 1024
12	Attention_3	Multi-Head Attention	1 × 4096 × 1024
13	Add_3	Add	1 × 4096 × 1024
14	LayerNorm_3_1	LayerNorm	1 × 4096 × 1024
15	FFN_3	Feed Forward	1 × 4096 × 1024
16	Attention_4	Multi-Head Attention	1 × 4096 × 1024
17	Add_4	Add	1 × 4096 × 1024
18	LayerNorm_4_1	LayerNorm	1 × 4096 × 1024
19	FFN_4	Feed Forward	1 × 4096 × 1024
20	Attention_5	Multi-Head Attention	1 × 4096 × 1024
21	Add_5	Add	1 × 4096 × 1024
22	LayerNorm_5_1	LayerNorm	1 × 4096 × 1024
23	FFN_5	Feed Forward	1 × 4096 × 1024
24	Attention_6	Multi-Head Attention	1 × 4096 × 1024
25	Add_6	Add	1 × 4096 × 1024
26	LayerNorm_6_1	LayerNorm	1 × 4096 × 1024
27	FFN_6	Feed Forward	1 × 4096 × 1024
28	Attention_7	Multi-Head Attention	1 × 4096 × 1024
29	Add_7	Add	1 × 4096 × 1024
30	LayerNorm_7_1	LayerNorm	1 × 4096 × 1024
31	FFN_7	Feed Forward	1 × 4096 × 1024
32	Attention_8	Multi-Head Attention	1 × 4096 × 1024
33	Add_8	Add	1 × 4096 × 1024
34	LayerNorm_8_1	LayerNorm	1 × 4096 × 1024
35	FFN_8	Feed Forward	1 × 4096 × 1024
36	Attention_9	Multi-Head Attention	1 × 4096 × 1024
37	Add_9	Add	1 × 4096 × 1024
38	LayerNorm_9_1	LayerNorm	1 × 4096 × 1024
39	FFN_9	Feed Forward	1 × 4096 × 1024
40	Attention_10	Multi-Head Attention	1 × 4096 × 1024
41	Add_10	Add	1 × 4096 × 1024
42	LayerNorm_10_1	LayerNorm	1 × 4096 × 1024
43	FFN_10	Feed Forward	1 × 4096 × 1024
44	Attention_11	Multi-Head Attention	1 × 4096 × 1024
45	Add_11	Add	1 × 4096 × 1024
46	LayerNorm_11_1	LayerNorm	1 × 4096 × 1024
47	FFN_11	Feed Forward	1 × 4096 × 1024
48	Attention_12	Multi-Head Attention	1 × 4096 × 1024
49	Add_12	Add	1 × 4096 × 1024
50	LayerNorm_12_1	LayerNorm	1 × 4096 × 1024
51	FFN_12	Feed Forward	1 × 4096 × 1024
52	Output	Output	1 × 4096 × 1024

What the verifier says

infoAt 12 stacked attention layers, residual-branch outputs add up; unscaled init lets activation variance grow with depth. GPT-2/LLaMA-family models scale the residual projections by depth (N(0, 0.02 / √(2L))). Fix: Scale residual output projections by depth: nn.init.normal_(w, std=0.02 / math.sqrt(2 * n_layers))
deep-attention-default-init

Do this to your own model

Same numbers, on a model in your repo, in one command. No account.

pip install neurarch-trace
neurarch-trace microsoft/Florence-2-large --plan --share

Paste a model instead Markdown twin Index JSON POST /api/v1/plan

Other florence2 checkpoints

Florence-2-base

82.7M derived · -64.3% against the checkpoint

Florence-2-base-ft

82.7M derived · -64.3% against the checkpoint

Florence-2-VQAJP2

82.7M derived · -69.5% against the checkpoint