fix(deps, pyamber): bump transformers from 5.3.0 to 5.13.1 in /amber

[dependabot]

Bumps transformers from 5.3.0 to 5.13.1.

Release notes

Sourced from transformers's releases.

Patch release v5.13.1

This patch is focused on enabling transformers for the latest release of vllm!

• Be more defensive with remap_legacy_layer_types for custom models (#47245) from @​hmellor
• Fix custom code which doesn't know about the new linear layer type names (#47174) from @​hmellor
• Fix case where _LazyAutoMapping.register is passed a str key (#47148) from @​hmellor

Release v5.13.0

New Model additions

KimiK 2.5, 2.6, and 2.7

This release includes the architecture for Kimi 2.5 which is used by 2.5-2.7:

Kimi K2.5 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration. The model was proposed in Kimi K2.5: Visual Agentic Intelligence and further improved in [Kimi K2.6: Advancing Open-Source Coding](https://github.com/huggingface/transformers/blob/HEAD/Kimi K2.5: Visual Agentic Intelligence).

Kimi K2.5 achieves significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming languages (Rust, Go, Python) and domains spanning front-end, DevOps, and performance optimization. The model is capable of transforming simple prompts and visual inputs into production-ready interfaces and lightweight full-stack workflows, generating structured layouts, interactive elements, and rich animations with deliberate aesthetic precision.

Links: Documentation

• Add new model: Kimi2-6 (#45630) by @​zucchini-nlp in #45630

MiMo-V2-Flash

MiMo-V2-Flash is a Mixture-of-Experts (MoE) language model developed by the Xiaomi MiMo team. Designed to establish a new balance between long-context modeling capabilities and inference efficiency, the model is built for strong performance in complex reasoning and agentic tasks. Trained on 27T tokens with native 32k sequence lengths, MiMo-V2-Flash seamlessly supports an extended 256K context window while significantly reducing KV-cache storage compared to standard global attention models.

Links: Documentation

• Add Xiaomi MiMo-V2 (#45144) by @​casinca in #45144

Nemotron 3.5 ASR

Nemotron 3.5 ASR is a 600M-parameter multilingual speech recognition model from NVIDIA, built for high-quality transcription in both low-latency streaming and high-throughput batch settings, with native punctuation and capitalization. For streaming, it offers configurable chunk sizes—80ms, 160ms, 560ms, and 1120ms, letting users trade off latency against accuracy to suit their application. Its cache-aware FastConformer-RNNT architecture is central to this capability: unlike traditional buffered streaming, which repeatedly reprocesses overlapping audio windows, the model processes only each new incoming chunk while reusing cached encoder context from prior chunks. This eliminates redundant computation, significantly improves efficiency, and minimizes end-to-end delay without sacrificing accuracy, making it well suited to real-time transcription workloads.

Links: Documentation

• Add Nemotron 3.5 ASR Streaming (#46565) by @​eustlb in #46565

NemotronAsrStreaming

Nemotron ASR Streaming is a 600M-parameter English speech recognition model from NVIDIA, built for high-quality transcription in both low-latency streaming and high-throughput batch settings, with native punctuation and capitalization. For streaming, it offers configurable chunk sizes—80ms, 160ms, 560ms, and 1120ms, letting users trade off latency against accuracy to suit their application. Its cache-aware FastConformer-RNNT architecture is central to this capability: unlike traditional buffered streaming, which repeatedly reprocesses overlapping audio windows, the model processes only each new incoming chunk while reusing cached encoder context from prior chunks. This eliminates redundant computation, significantly improves efficiency, and minimizes end-to-end delay without sacrificing accuracy, making it well suited to real-time transcription workloads.

Links: Documentation

• Add Nemotron ASR Streaming (#46332) by @​eustlb in #46332

... (truncated)

Commits

• 4626421 v5.13.1
• bef013c Be more defensive with remap_legacy_layer_types for custom models (#47245)
• 8bbbe24 Fix custom code which doesn't know about the new linear layer type names (#47...
• c782b84 Fix case where _LazyAutoMapping.register is passed a str key (#47148)
• 6af945f v5.13.0
• 0983570 Add new model: Kimi2-6 (#45630)
• 600c196 Update workflow callers to use transformers-ci (#47040)
• 259711a Fix/qwen3 variants tp plan (#47041)
• 5d06305 Add HunYuan VL model (#46417)
• 89917d0 Add tiny_model_id support to ProcessorTesterMixin for memory-sensitive tests ...
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[github-actions]

Automated Reviewer Suggestions

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

• Contributors with relevant context: @aglinxinyuan
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[github-actions]

✅ No material benchmark regressions detected

🟢 7 better · 🔴 0 worse · ⚪ 8 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	436	0.266	22,603/28,207/28,207 us	🟢 -16.4% / ⚪ within ±5%
🟢	bs=100 sw=10 sl=64	912	0.557	108,936/131,810/131,810 us	🟢 -11.8% / ⚪ within ±5%
⚪	bs=1000 sw=10 sl=64	1,104	0.674	906,971/945,047/945,047 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	436 tuples/sec	412 tuples/sec	n/a	+5.8%	n/a
bs=10 sw=10 sl=64	MB/s	0.266 MB/s	0.251 MB/s	n/a	+6.0%	n/a
bs=10 sw=10 sl=64	p50	22,603 us	24,804 us	n/a	-8.9%	n/a
bs=10 sw=10 sl=64	p95	28,207 us	33,721 us	n/a	-16.4%	n/a
bs=10 sw=10 sl=64	p99	28,207 us	33,721 us	n/a	-16.4%	n/a
bs=100 sw=10 sl=64	throughput	912 tuples/sec	914 tuples/sec	n/a	-0.2%	n/a
bs=100 sw=10 sl=64	MB/s	0.557 MB/s	0.558 MB/s	n/a	-0.2%	n/a
bs=100 sw=10 sl=64	p50	108,936 us	108,389 us	n/a	+0.5%	n/a
bs=100 sw=10 sl=64	p95	131,810 us	149,371 us	n/a	-11.8%	n/a
bs=100 sw=10 sl=64	p99	131,810 us	149,371 us	n/a	-11.8%	n/a
bs=1000 sw=10 sl=64	throughput	1,104 tuples/sec	1,111 tuples/sec	n/a	-0.6%	n/a
bs=1000 sw=10 sl=64	MB/s	0.674 MB/s	0.678 MB/s	n/a	-0.6%	n/a
bs=1000 sw=10 sl=64	p50	906,971 us	899,019 us	n/a	+0.9%	n/a
bs=1000 sw=10 sl=64	p95	945,047 us	963,123 us	n/a	-1.9%	n/a
bs=1000 sw=10 sl=64	p99	945,047 us	963,123 us	n/a	-1.9%	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
0,10,10,64,20,459.09,200,128000,436,0.266,22603.30,28207.03,28207.03
1,100,10,64,20,2192.51,2000,1280000,912,0.557,108935.83,131810.28,131810.28
2,1000,10,64,20,18111.56,20000,12800000,1104,0.674,906970.62,945047.42,945047.42