Models / gpt_neox

pythia-160m

Reconstructed from its own config.json with no weights read. 3.4M 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

164M

163,946,112 parameters

In the published checkpoint

213M

212,654,688 scalars · safetensors.total, read 2026-09-06

Delta

-22.9%

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

$10.96

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

Structure

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

	Layer	Type	Output shape
1	Input	Input	1 × 2048
2	Embedding	Embedding	1 × 2048 × 768
3	Positional_Embedding	Learned Pos Embed	1 × 2048 × 768
4	LayerNorm_1_1	LayerNorm	1 × 2048 × 768
5	Attention_1	Multi-Head Attention	1 × 2048 × 768
6	Add_1_attn	Add	1 × 2048 × 768
7	LayerNorm_1_2	LayerNorm	1 × 2048 × 768
8	FFN_1	Feed Forward	1 × 2048 × 768
9	Add_1_ffn	Add	1 × 2048 × 768
10	LayerNorm_2_1	LayerNorm	1 × 2048 × 768
11	Attention_2	Multi-Head Attention	1 × 2048 × 768
12	Add_2_attn	Add	1 × 2048 × 768
13	LayerNorm_2_2	LayerNorm	1 × 2048 × 768
14	FFN_2	Feed Forward	1 × 2048 × 768
15	Add_2_ffn	Add	1 × 2048 × 768
16	LayerNorm_3_1	LayerNorm	1 × 2048 × 768
17	Attention_3	Multi-Head Attention	1 × 2048 × 768
18	Add_3_attn	Add	1 × 2048 × 768
19	LayerNorm_3_2	LayerNorm	1 × 2048 × 768
20	FFN_3	Feed Forward	1 × 2048 × 768
21	Add_3_ffn	Add	1 × 2048 × 768
22	LayerNorm_4_1	LayerNorm	1 × 2048 × 768
23	Attention_4	Multi-Head Attention	1 × 2048 × 768
24	Add_4_attn	Add	1 × 2048 × 768
25	LayerNorm_4_2	LayerNorm	1 × 2048 × 768
26	FFN_4	Feed Forward	1 × 2048 × 768
27	Add_4_ffn	Add	1 × 2048 × 768
28	LayerNorm_5_1	LayerNorm	1 × 2048 × 768
29	Attention_5	Multi-Head Attention	1 × 2048 × 768
30	Add_5_attn	Add	1 × 2048 × 768
31	LayerNorm_5_2	LayerNorm	1 × 2048 × 768
32	FFN_5	Feed Forward	1 × 2048 × 768
33	Add_5_ffn	Add	1 × 2048 × 768
34	LayerNorm_6_1	LayerNorm	1 × 2048 × 768
35	Attention_6	Multi-Head Attention	1 × 2048 × 768
36	Add_6_attn	Add	1 × 2048 × 768
37	LayerNorm_6_2	LayerNorm	1 × 2048 × 768
38	FFN_6	Feed Forward	1 × 2048 × 768
39	Add_6_ffn	Add	1 × 2048 × 768
40	LayerNorm_7_1	LayerNorm	1 × 2048 × 768
41	Attention_7	Multi-Head Attention	1 × 2048 × 768
42	Add_7_attn	Add	1 × 2048 × 768
43	LayerNorm_7_2	LayerNorm	1 × 2048 × 768
44	FFN_7	Feed Forward	1 × 2048 × 768
45	Add_7_ffn	Add	1 × 2048 × 768
46	LayerNorm_8_1	LayerNorm	1 × 2048 × 768
47	Attention_8	Multi-Head Attention	1 × 2048 × 768
48	Add_8_attn	Add	1 × 2048 × 768
49	LayerNorm_8_2	LayerNorm	1 × 2048 × 768
50	FFN_8	Feed Forward	1 × 2048 × 768
51	Add_8_ffn	Add	1 × 2048 × 768
52	LayerNorm_9_1	LayerNorm	1 × 2048 × 768
53	Attention_9	Multi-Head Attention	1 × 2048 × 768
54	Add_9_attn	Add	1 × 2048 × 768
55	LayerNorm_9_2	LayerNorm	1 × 2048 × 768
56	FFN_9	Feed Forward	1 × 2048 × 768
57	Add_9_ffn	Add	1 × 2048 × 768
58	LayerNorm_10_1	LayerNorm	1 × 2048 × 768
59	Attention_10	Multi-Head Attention	1 × 2048 × 768
60	Add_10_attn	Add	1 × 2048 × 768
61	LayerNorm_10_2	LayerNorm	1 × 2048 × 768
62	FFN_10	Feed Forward	1 × 2048 × 768
63	Add_10_ffn	Add	1 × 2048 × 768
64	LayerNorm_11_1	LayerNorm	1 × 2048 × 768
65	Attention_11	Multi-Head Attention	1 × 2048 × 768
66	Add_11_attn	Add	1 × 2048 × 768
67	LayerNorm_11_2	LayerNorm	1 × 2048 × 768
68	FFN_11	Feed Forward	1 × 2048 × 768
69	Add_11_ffn	Add	1 × 2048 × 768
70	LayerNorm_12_1	LayerNorm	1 × 2048 × 768
71	Attention_12	Multi-Head Attention	1 × 2048 × 768
72	Add_12_attn	Add	1 × 2048 × 768
73	LayerNorm_12_2	LayerNorm	1 × 2048 × 768
74	FFN_12	Feed Forward	1 × 2048 × 768
75	Add_12_ffn	Add	1 × 2048 × 768
76	Final_LayerNorm	LayerNorm	1 × 2048 × 768
77	LM_Head	Linear	1 × 2048 × 50304
78	Output	Output	1 × 2048 × 50304

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 EleutherAI/pythia-160m --plan --share

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