Models / molformer

MoLFormer-XL-both-10pct: GPU memory to fine-tune

Fine-tuning MoLFormer-XL-both-10pct at batch 1 and 202 tokens takes about 0.89 GB of GPU memory for a full fine-tune (fits one T4 16 GB), and 0.16 GB with LoRA r=16 on a bf16 base (fits one T4 16 GB). Estimated from the model's structure, reading the config under transformers semantics, not measured. What fits on which card.

Reconstructed from its own config.json with no weights read. 102K 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

44.5M

44,510,208 parameters

In the published checkpoint

46.8M

46,805,760 scalars · safetensors.total, read 2026-07-23

Delta

-4.90%

custom-code This repository ships its own modeling code (`auto_map`, e.g. `configuration_molformer.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, 202 tokens over 50,000 samples (assumed; no dataset attached).

Layers

Will it forward-pass

Yes

Priced on

A10G (24GB)

Est. one run

$0.38

Memory: what fits on which card

Largest batch per card. Training is sized at 202 tokens per sample, serving at 202 tokens per sequence.

Full fine-tune, batch 1 at 202 tokens

0.89 GB

LoRA r=16, batch 1 at 202 tokens

0.16 GB

Serve: bf16 weights

89 MB

Serve: KV cache, 202 tokens

7.4 MB

Card	Full fine-tune	LoRA r=16	Serve bf16, 202 tokens
T4 16 GB	batch 380	batch 402	batch 1856
L4 24 GB	batch 583	batch 605	batch 2790
A100 40 GB	batch 989	batch 1011	batch 4659
A100 / H100 80 GB	batch 2004	batch 2026	batch 9330
H200 141 GB	batch 3552	batch 3573	batch 16453

Training: 202 tokens per sample (2048, or the model's context when shorter), AdamW with bf16 autocast, SDPA attention (no score matrix stored), 1.15x headroom on every card. Full fine-tune holds weights, gradients and optimizer state (1.2 x 16 bytes per parameter) plus activations. LoRA is r=16 on every linear layer but the head, base frozen in bf16 (2 bytes per parameter), adapters at full AdamW state, same activations. A two-card split is a contiguous layer cut (pipeline parallel), computed only where one card cannot hold batch 1. Serving: bf16 weights plus the KV cache of 202 tokens per sequence; prefill working set, CUDA context and framework reserve not counted.

These are estimates from the graph, not measurements. Against measured runs, held out by architecture, the estimate flagged 113 of the 131 jobs that did not fit their card and said "fits" on the other 18. Every measured run.

Structure

52 nodes. Output shapes are propagated from the input shape, batch dimension excluded, at 202 tokens: the config's maximum context.

	Layer	Type	Output shape
1	Input	Input	1 × 202
2	Embedding	Embedding	1 × 202 × 768
3	Positional_Embedding	Learned Pos Embed	1 × 202 × 768
4	Attention_1	Multi-Head Attention	1 × 202 × 768
5	Add_1	Add	1 × 202 × 768
6	LayerNorm_1_1	LayerNorm	1 × 202 × 768
7	FFN_1	Feed Forward	1 × 202 × 768
8	Attention_2	Multi-Head Attention	1 × 202 × 768
9	Add_2	Add	1 × 202 × 768
10	LayerNorm_2_1	LayerNorm	1 × 202 × 768
11	FFN_2	Feed Forward	1 × 202 × 768
12	Attention_3	Multi-Head Attention	1 × 202 × 768
13	Add_3	Add	1 × 202 × 768
14	LayerNorm_3_1	LayerNorm	1 × 202 × 768
15	FFN_3	Feed Forward	1 × 202 × 768
16	Attention_4	Multi-Head Attention	1 × 202 × 768
17	Add_4	Add	1 × 202 × 768
18	LayerNorm_4_1	LayerNorm	1 × 202 × 768
19	FFN_4	Feed Forward	1 × 202 × 768
20	Attention_5	Multi-Head Attention	1 × 202 × 768
21	Add_5	Add	1 × 202 × 768
22	LayerNorm_5_1	LayerNorm	1 × 202 × 768
23	FFN_5	Feed Forward	1 × 202 × 768
24	Attention_6	Multi-Head Attention	1 × 202 × 768
25	Add_6	Add	1 × 202 × 768
26	LayerNorm_6_1	LayerNorm	1 × 202 × 768
27	FFN_6	Feed Forward	1 × 202 × 768
28	Attention_7	Multi-Head Attention	1 × 202 × 768
29	Add_7	Add	1 × 202 × 768
30	LayerNorm_7_1	LayerNorm	1 × 202 × 768
31	FFN_7	Feed Forward	1 × 202 × 768
32	Attention_8	Multi-Head Attention	1 × 202 × 768
33	Add_8	Add	1 × 202 × 768
34	LayerNorm_8_1	LayerNorm	1 × 202 × 768
35	FFN_8	Feed Forward	1 × 202 × 768
36	Attention_9	Multi-Head Attention	1 × 202 × 768
37	Add_9	Add	1 × 202 × 768
38	LayerNorm_9_1	LayerNorm	1 × 202 × 768
39	FFN_9	Feed Forward	1 × 202 × 768
40	Attention_10	Multi-Head Attention	1 × 202 × 768
41	Add_10	Add	1 × 202 × 768
42	LayerNorm_10_1	LayerNorm	1 × 202 × 768
43	FFN_10	Feed Forward	1 × 202 × 768
44	Attention_11	Multi-Head Attention	1 × 202 × 768
45	Add_11	Add	1 × 202 × 768
46	LayerNorm_11_1	LayerNorm	1 × 202 × 768
47	FFN_11	Feed Forward	1 × 202 × 768
48	Attention_12	Multi-Head Attention	1 × 202 × 768
49	Add_12	Add	1 × 202 × 768
50	LayerNorm_12_1	LayerNorm	1 × 202 × 768
51	FFN_12	Feed Forward	1 × 202 × 768
52	Output	Output	1 × 202 × 768

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

What changed upstream

The change tracker follows ibm-research/MoLFormer-XL-both-10pct: every revision it has read, what changed in the config, tokenizer, chat template or weights between them, and an OSV advisory per change.

Revision history and changes Badge JSON OSV advisories All tracked changes

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 ibm-research/MoLFormer-XL-both-10pct --plan --share

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