fix(deps, pyamber): bump transformers from 5.3.0 to 5.5.0 in /amber

[dependabot]

Bumps transformers from 5.3.0 to 5.5.0.

Release notes

Sourced from transformers's releases.

Release v5.5.0

New Model additions

Gemma4

Gemma 4 is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis.

You can find all the original Gemma 4 checkpoints under the Gemma 4 release.

The key difference from previous Gemma releases is the new design to process images of different sizes using a fixed-budget number of tokens. Unlike many models that squash every image into a fixed square (like 224×224), Gemma 4 keeps the image's natural aspect ratio while making it the right size. There a a couple constraints to follow:

• The total number of pixels must fit within a patch budget
• Both height and width must be divisible by 48 (= patch size 16 × pooling kernel 3)

[!IMPORTANT] Gemma 4 does not apply the standard ImageNet mean/std normalization that many other vision models use. The model's own patch embedding layer handles the final scaling internally (shifting values to the [-1, 1] range).

The number of "soft tokens" (aka vision tokens) an image processor can produce is configurable. The supported options are outlined below and the default is 280 soft tokens per image.

Soft Tokens	Patches (before pooling)	Approx. Image Area
70	630	~161K pixels
140	1,260	~323K pixels
280	2,520	~645K pixels
560	5,040	~1.3M pixels
1,120	10,080	~2.6M pixels

To encode positional information for each patch in the image, Gemma 4 uses a learned 2D position embedding table. The position table stores up to 10,240 positions per axis, which allows the model to handle very large images. Each position is a learned vector of the same dimensions as the patch embedding. The 2D RoPE which Gemma 4 uses independently rotate half the attention head dimensions for the x-axis and the other half for the y-axis. This allows the model to understand spatial relationships like "above," "below," "left of," and "right of."

NomicBERT

NomicBERT is a BERT-inspired encoder model that applies Rotary Position Embeddings (RoPE) to create reproducible long context text embeddings. It is the first fully reproducible, open-source text embedding model with 8192 context length that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short-context MTEB and long context LoCo benchmarks. The model generates dense vector embeddings for various tasks including search, clustering, and classification using specific instruction prefixes.

Links: Documentation | Paper

• Internalise the NomicBERT model (#43067) by @​ed22699 in #43067

MusicFlamingo

Music Flamingo is a fully open large audio–language model designed for robust understanding and reasoning over music. It builds upon the Audio Flamingo 3 architecture by including Rotary Time Embeddings (RoTE), which injects temporal position information to enable the model to handle audio sequences up to 20 minutes. The model features a unified audio encoder across speech, sound, and music with special sound boundary tokens for improved audio sequence modeling.

Links: Documentation | Paper

• Add Music Flamingo (#43538) by @​lashahub in #43538

... (truncated)

Commits

• c1c3424 update
• 20bff68 update release workflow
• 8956441 v5.5.0
• 5135e5e casually dropping the most capable open weights on the planet (#45192)
• a594e09 Internalise the NomicBERT model (#43067)
• 4932e97 Fix resized LM head weights being overwritten by post_init (#45079)
• 57e8413 [Qwen3.5 MoE] Add _tp_plan to ForConditionalGeneration (#45124)
• b10552e Fix TypeError: 'NoneType' object is not iterable in GenerationMixin.generate ...
• 423f2a3 fix(models): Fix dtype mismatch in SwitchTransformers and TimmWrapperModel (#...
• ade7a05 Generalize gemma vision mask to videos (#45185)
• Additional commits viewable in compare view

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[github-actions]

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Based on the git blame history of the changed files, we recommend the following reviewers:

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[github-actions]

⚠️ Benchmark changes need a look

🟢 1 better · 🔴 2 worse · ⚪ 12 noise (<±5%) · 0 without baseline

Compared against main e029e0d benchmarked on this same runner, so the delta is largely free of cross-runner hardware noise. The "7d avg" column still reflects the gh-pages dashboard. Treat <±5% as noise unless repeated.

Dashboard · Run

	config	throughput	MB/s	latency	max Δ latest / 7d
🔴	bs=10 sw=10 sl=64	429	0.262	22,082/36,730/36,730 us	🔴 +12.8% / ⚪ within ±5%
⚪	bs=100 sw=10 sl=64	971	0.593	102,009/128,832/128,832 us	⚪ within ±5% / ⚪ within ±5%
⚪	bs=1000 sw=10 sl=64	1,081	0.66	926,535/966,234/966,234 us	⚪ within ±5% / ⚪ within ±5%

Baseline details

Latest main e029e0d from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	429 tuples/sec	422 tuples/sec	n/a	+1.7%	n/a
bs=10 sw=10 sl=64	MB/s	0.262 MB/s	0.258 MB/s	n/a	+1.6%	n/a
bs=10 sw=10 sl=64	p50	22,082 us	23,703 us	n/a	-6.8%	n/a
bs=10 sw=10 sl=64	p95	36,730 us	32,550 us	n/a	+12.8%	n/a
bs=10 sw=10 sl=64	p99	36,730 us	32,550 us	n/a	+12.8%	n/a
bs=100 sw=10 sl=64	throughput	971 tuples/sec	956 tuples/sec	n/a	+1.6%	n/a
bs=100 sw=10 sl=64	MB/s	0.593 MB/s	0.583 MB/s	n/a	+1.7%	n/a
bs=100 sw=10 sl=64	p50	102,009 us	99,878 us	n/a	+2.1%	n/a
bs=100 sw=10 sl=64	p95	128,832 us	124,381 us	n/a	+3.6%	n/a
bs=100 sw=10 sl=64	p99	128,832 us	124,381 us	n/a	+3.6%	n/a
bs=1000 sw=10 sl=64	throughput	1,081 tuples/sec	1,104 tuples/sec	n/a	-2.1%	n/a
bs=1000 sw=10 sl=64	MB/s	0.66 MB/s	0.674 MB/s	n/a	-2.1%	n/a
bs=1000 sw=10 sl=64	p50	926,535 us	908,563 us	n/a	+2.0%	n/a
bs=1000 sw=10 sl=64	p95	966,234 us	945,853 us	n/a	+2.2%	n/a
bs=1000 sw=10 sl=64	p99	966,234 us	945,853 us	n/a	+2.2%	n/a

Raw CSV

config_idx,batch_size,schema_width,string_len,num_batches,total_ms,total_tuples,total_bytes,tuples_per_sec,mb_per_sec,lat_p50_us,lat_p95_us,lat_p99_us
0,10,10,64,20,465.92,200,128000,429,0.262,22082.39,36730.16,36730.16
1,100,10,64,20,2059.56,2000,1280000,971,0.593,102009.18,128831.92,128831.92
2,1000,10,64,20,18503.42,20000,12800000,1081,0.660,926535.19,966233.91,966233.91