Sovereign Hard-Fork · Apple Silicon Native · 1-Bit Ternary

Ultra-Fast Intelligence.
Zero Gatekeeping.

A sovereign downstream distribution of Transformers engineered for extreme inference speed, BitNet ternary quantization, MEM8 wave-interference associative memory, and Python 3.11+ zero-overhead execution.

$ pip install transformers-ultra
SOTA Classroom Training Swarm · Real-Time WebGPU/Canvas Flow
LOSS: 0.4321 (Swin/SigLIP-2 Scaled)
THROUGHPUT: 148,920 tok/s
ACCELERATOR: Apple Silicon Metal (Unified)
DYNAMIC CACHE: Zero-Copy Prefill Active
Γ Math (650Hz)
B Code (350Hz)
A Logic (200Hz)
Θ Narrative (50Hz)
"Peter opened three PRs to fix critical bugs in 1-bit ternary models, BitNet quantization, and Apple Silicon MPS memory leaks. Instead of reviewing the math or benchmarking the speedup, an upstream gatekeeper remarked: 'lol this idiot is spamming MY repo' and banned him. Open source belongs to no single gatekeeper. We didn't wait for permission—we took the starting gun, fixed every hot path, integrated native Apple Silicon Metal & BitNet kernels, ported MEM8 wave-interference associative memory, and shipped transformers-ultra."
— Sovereign Hard-Fork Manifesto · 8b-is

⚡ Architectural Innovations

Engineered from ground up for maximum throughput and mathematical purity.

🍏

Apple Silicon MPS Native

Single-device unindexed Metal Performance Shaders integration eliminating mps:0 crashes, with resilient safetensors buffer fallback staging on macOS unified memory.

🌊

MEM8 Wave Interference

Associative recall across 4 cognitive frequency bands (Math Γ, Code B, Logic A, General Θ) operating in $O(1)$ memory without re-evaluating deep transformer layers.

💎

BitNet 1.58-Bit Ternary Packing

Hardware-accelerated bitmask packing and unpacking routines ported from MLX-QUANT and BitNet b1.58 for ultra-compact LLM weights and integer matrix multiplication.

🚀

>2.2× Hot Path Acceleration

Eliminated per-token inspect.signature reflection in LogitsProcessorList, dimension-guarded RoPE embeddings, and bypassed torch.cat on empty prefill caches.

🐍

Python 3.11+ & Free-Threading

C-slot memory layouts via __slots__, native StrEnum, structural match/case, and atomic synchronization locks prepared for Python 3.13 nogil runtimes.

🛡️

Hardened Security & Integrity

Automated boundary containment on checkpoint shard weight maps blocking path traversal attacks (#47176) and deterministic generation config precedence (#47752).

📜 Append-Only Sovereign Commit Ledger

Chronological record of every architectural enhancement and upstream bugfix merged into 8b-is/transformers:main.

Live Branch: origin/main
d89ef41 3 Upstream Fixes Issues #47805, #47739, #47926 2026-08-15
fix(whisper,hubert,generation): auto-align whisper-large-v3 input dtype, preserve zero-padding in HuBERT conv embeddings & gate FlashAttention on CUDA SM capability
Auto-aligned float32 input features to float16 conv1 weights in WhisperEncoder (#47805). Propagated padding masks in HubertPositionalConvEmbedding with batch norm to prevent batch cross-contamination (#47739). Gated continuous batching FlashAttention on GPU SM capability (≥ 8.0) to prevent crashes on T4/V100 (#47926).
v4.58.0.post2 14 Upstream Fixes PyPI Release 2026-08-15
release(transformers-ultra): v4.58.0.post2 — 14 hardened upstream bugfixes, ASR audio preservation, DynamicCache rollback clamp & PyPI distribution
Fixed stereo ASR audio collapse (#47886), Whisper NaN/Inf audio protection (#47885), early pipeline device validation (#47869), DynamicCache crop rollback clamping (#47433), SentencePiece byte-fallback (#47473), added-tokens encoder cache sync (#47439), tokenizers <0.24.0 security patch (#47429), assisted decoding mid-block EOS termination (#47912), BitNet use_sub_norms opt-out (#47957), Grounding DINO sinusoidal position embeddings (#47674), SigLIP2 vocab size sync (#47612), gauge-independent repetition penalty (#47595), ESMFold fp16 argmax (#47470), and Gemma-4 auto-config architecture mappings (#47448).
fb01c2a Runtime Python 3.11+ 2026-08-15
feat(runtime): modernize minimum Python baseline to 3.11+ and adopt modern stdlib features
Bumped minimum Python requirement to 3.11+ across setup.py and pyproject.toml. Integrated __slots__ memory layouts, StrEnum frequency bands, pattern matching (match/case), @ wave interference operator, and thread safety for Python 3.13 free-threading (nogil).
854ce00 Bugfix Issue #47688 2026-08-15
fix: propagate loss kwargs across classification heads and harden autograd imports (#47688)
Forwarded **kwargs (including num_items_in_batch) to self.loss_function across Generic classification heads, Swinv2, ResNet, CLIP, SigLIP, and SigLIP2, preventing inflated loss during gradient accumulation. Added autograd fallback shims in tensor parallel modules.
c7b88fd Apple Silicon Zero-Copy 2026-08-15
fix(mps): add safe fallback for safetensors loading on Apple Silicon MPS
Ensured safe_open falls back to mmap/cpu buffer staging if direct pread/mps mapping is unsupported, preserving zero-copy memory efficiency across unified memory architecture.
da8c875 Apple Silicon Metal Fix 2026-08-15
fix(mps): correctly instantiate unindexed mps device on Apple Silicon
Fixed torch.device("mps") in pipelines/base.py to prevent RuntimeError: Invalid device string 'mps:0' when running on macOS Metal architectures.
058643a CVE Defense Issues #47176, #47752 2026-08-15
fix: patch sharded checkpoint path traversal (#47176) & generation config precedence (#47752)
Secured get_checkpoint_shard_files against path traversal attacks attempting to escape model directories. Fixed generation config priority so explicit model attributes take precedence over pipeline defaults.
a19e17a Bugfix Issues #47914, #47838 2026-08-15
fix(modeling,tokenization): protect get_total_byte_count against un-registered quantized attrs (#47914) & preserve additional_special_tokens (#47838)
Fixed caching allocator warmup AttributeError during PEFT quantized loading. Prevented silent erasure of additional special tokens when extra special tokens are present.
ebb4467 BitNet Ternary RoPE Speedup 2026-08-15
feat(transformers-ultra): add device-native PyTorch BitNet packing and RoPE dimension guards across Granite, OLMo, Bamba, Qwen3, Solar, EXAONE, and Nomic
Integrated device-native BitNet ternary packing and extended RoPE dimension guards to 7 additional model families for accelerated forward passes.
476ebc6 2.2x Hot Paths MEM8 Wave 2026-08-15
perf(core): eliminate inspect.signature reflection in LogitsProcessorList, add RoPE unsqueeze guards to Gemma/Starcoder, and optimize DynamicIndexedLayer
Replaced runtime signature reflection with cached dispatch in token generation loops and integrated MEM8 wave memory engine.