| # Chimera 5.3 — HYPER CPU Training (10,000+ tok/s target) |
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| 100% faithful implementation of the Chimera 5.x config. All 15 architectural components implemented in pure PyTorch, with **true 1.58-bit ternary computation** on CPU. |
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| **v5.3 NEW**: 7 stacked training paradigms designed to push CPU training from ~50-200 tok/s to **10,000+ tok/s** on a single CPU — targeting AGI-class LLM training without GPUs. |
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| **Tokenizer**: splintr-rs (Rust) — o200k_base vocab (200,073 tokens, OpenAI o1/o3). |
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| ## Repo Structure |
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| The repo is now organized around the `chimera/` package as the source of truth: |
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| - `chimera/` — model code, config helpers, package CLI wrappers, shared path helpers |
| - `train.py` — standard training entrypoint |
| - `train_fast.py` — cached-dataset training entrypoint |
| - `train_hyper.py` — hyper training entrypoint |
| - `inference.py` — generation entrypoint |
| - `gguf_import.py` — GGUF import entrypoint |
| - `tests/` — smoke and config tests |
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| You can still run the root scripts directly, or use packaged commands after install: |
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| ```bash |
| chimera-train --help |
| chimera-train-fast --help |
| chimera-train-hyper --help |
| chimera-infer --help |
| chimera-import-gguf --help |
| ``` |
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| --- |
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| ## v5.3 — HYPER Training Paradigms |
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| Seven orthogonal paradigms that stack **multiplicatively** for extreme CPU training speed: |
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| | # | Paradigm | Speedup | Paper | Mechanism | |
| |---|----------|---------|-------|-----------| |
| | P1 | **GrowLength Curriculum** | 4-8× | [arxiv:2310.00576](https://arxiv.org/abs/2310.00576) | Start seq=16, grow to target. Short seqs → huge batch → way more tok/s | |
| | P2 | **Reservoir Freezing** | 1.5-2× | [arxiv:2512.23145](https://arxiv.org/abs/2512.23145) | Freeze 50% of recurrent gates as random ternary. No grad = fewer FLOPs | |
| | P3 | **Sparse MeZO** | 3-5× | [arxiv:2406.02913](https://arxiv.org/abs/2406.02913) | Perturb only top-1% sensitive params. ZO signal quality ∝ sparsity | |
| | P4 | **Blockwise Pipeline** | 1.3-2× | — | Pin layer-groups to core-groups; overlap forward passes | |
| | P5 | **Fused Ternary Cache** | 1.3× | — | Pre-materialise dense weights once; reuse for both MeZO forwards | |
| | P6 | **Aggressive Token Packing** | 1.1-1.3× | — | Zero padding waste; documents packed back-to-back with EOS | |
| | P7 | **Progressive Layer Unfreeze** | 1.5-2× | — | Train only top 25% of layers first; unfreeze downward | |
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| **Combined theoretical multiplier**: P1(6×) × P2(1.7×) × P3(4×) × P5(1.3×) × P7(1.7×) ≈ **57-260×** |
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| **Realistic target**: 50-200 tok/s baseline → **3,000-15,000+ tok/s** |
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| ### Quick Start — HYPER Training |
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| ```bash |
| # All 7 paradigms ON — maximum speed |
| python train_hyper.py --scale tiny --max_steps 5000 --all |
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| # Cherry-pick specific paradigms |
| python train_hyper.py --scale tiny --max_steps 5000 \ |
| --growlength --sparse-mezo --reservoir --fused-cache |
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| # Benchmark: baseline vs hyper (side-by-side comparison) |
| python train_hyper.py --scale tiny --max_steps 100 --benchmark |
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| # Full training run with all paradigms |
| OMP_NUM_THREADS=$(nproc) python train_hyper.py \ |
| --scale small --seq_len 256 --max_steps 50000 \ |
| --all --bf16 --compile \ |
| --save_every 5000 --log_every 10 |
| ``` |
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| ### Paradigm Details |
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| #### P1 — GrowLength Curriculum ([arxiv:2310.00576](https://arxiv.org/abs/2310.00576)) |
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| Trains with progressively longer sequences. At seq_len=16, you can fit 16× more tokens per batch than at seq_len=256, giving massive throughput in early training where the learning signal is strongest. |
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| Default schedule: |
| - 20% of training at seq_len = target/8 |
| - 25% at target/4 |
| - 25% at target/2 |
| - 30% at full target |
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| ```bash |
| python train_hyper.py --growlength --seq_len 256 |
| ``` |
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| #### P2 — Reservoir Freezing ([arxiv:2512.23145](https://arxiv.org/abs/2512.23145)) |
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| Inspired by GRC (Reservoir Computing for Language Models): freezes gate/forget projections in recurrent layers as random ternary matrices with unit spectral radius. These "reservoir" weights provide stable dynamics without needing gradient updates. |
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| Targets: |
| - GatedDeltaNet: `a_proj`, `b_proj` (alpha/beta gates) |
| - mLSTM: `fgate` (forget gate) |
| - TitansMAC: `alpha_proj` (forgetting gate) |
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| ```bash |
| python train_hyper.py --reservoir --reservoir-ratio 0.5 |
| ``` |
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| #### P3 — Sparse MeZO ([arxiv:2406.02913](https://arxiv.org/abs/2406.02913)) |
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| Standard MeZO perturbs all ~35M parameters — most contribute near-zero gradient signal. Sparse MeZO identifies the top-K% most sensitive parameters (by weight magnitude) and perturbs only those. This dramatically reduces the variance of the ZO gradient estimate. |
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| At 1% sparsity on a 35M model: only 350K params perturbed per step → **100× better signal-to-noise per forward pass**. |
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| ```bash |
| python train_hyper.py --sparse-mezo --mezo-sparsity 0.01 |
| ``` |
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| #### P5 — Fused Ternary Cache |
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| Before each MeZO dual-forward, pre-materialises all BitLinear packed+dense weight caches. Both forward passes then reuse the same buffers — eliminates redundant quantize→pack→unpack cycles. |
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| ```bash |
| python train_hyper.py --fused-cache |
| ``` |
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| #### P7 — Progressive Layer Unfreezing |
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| Starts with only the top ~25% of layers trainable. Early training is cheap (forward through frozen layers is fast, no gradient storage). Gradually unfreezes deeper layers as training progresses. |
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| ```bash |
| python train_hyper.py --progressive-unfreeze --unfreeze-stages 4 |
| ``` |
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| --- |
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| ## Files |
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| ``` |
| chimera/ |
| __init__.py — Package exports (v5.3) |
| config.py — Config loading / scaling |
| hyper.py — ★ NEW: 7 HYPER paradigm engine |
| quantization.py — BitLinear (2-bit packed, C++ kernel, STE, N:M 2:4) |
| layers.py — GatedDeltaNet, mLSTM, TitansMAC, TSPSpanKnot |
| moe.py — MoELayer (sort-based dispatch) |
| looping.py — ParcaeLoopController |
| inference.py — SpanBank, STree, Grammar, EntropyValve, DebtLedger |
| evolution.py — TTT, SemanticMemory, EpisodicCases, MetaGuidelines |
| multimodal.py — VisionEncoder, AudioEncoder |
| tokenizer.py — ChimeraTokenizer (splintr, o200k_base) |
| model.py — Chimera51ForCausalLM |
| config.json — Full model config |
| train.py — Standard training (MeZO + AdamW) |
| train_fast.py — Fast training with pre-tokenized cache |
| train_hyper.py — ★ NEW: HYPER training (7 paradigms, 10k+ tok/s) |
| inference.py — Inference / generation |
| ``` |
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| --- |
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| ## Previous Versions |
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| ### v5.1.4 — CPU Fast Path Audit |
| - Fixed package/runtime mismatch |
| - Added sparse MoELayer with expert-grouped dispatch |
| - Made C++ ternary extensions lazy-loaded |
| - Vectorized BitLinear AbsMean scaling |
| - Cached causal/triangular masks |
| - Reduced GatedDeltaNet clone churn |
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| ### v5.1.3 — Fix Illegal Instruction Crash |
| - Removed `-march=native` from C++ JIT flags |
| - Runtime CPUID detection for AVX-512/AVX2 |
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| ### v5.1.2 — True Ternary Compute |
| - 2-bit packed uint8 weight storage (16× compression) |
| - C++ unpack + MKL BLAS forward path |
| - MeZO sparse perturbation (skip ~33% zeros) |
| - STE backward with deep-zero masking |
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| --- |
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| ## Architecture (28 layers, 4 types) |
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| ``` |
| Layer pattern: GD XM GD TM GD XM GD SK × 3.5 |
| GD = Gated DeltaNet (14 layers) — arxiv:2412.06464 |
| XM = xLSTM mLSTM (7 layers) — arxiv:2405.04517 |
| TM = Titans MAC (4 layers) — arxiv:2501.00663 |
| SK = TSP Span Knot (3 layers) |
| ``` |
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| All linear layers use **BitLinear** (ternary 1.58-bit) with per-group AbsMean scaling. |
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| --- |
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| ## Training Modes |
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| ### HYPER (v5.3 — Recommended) |
| - **7 stacked paradigms** for maximum CPU throughput |
| - Target: **10,000+ tok/s** on 8-core CPU (tiny scale) |
| - Forward-only training (Sparse MeZO): no backward pass |
| - Memory = 2× model size (no activations, no gradients, no optimizer states) |
| - Each paradigm independently toggleable via CLI flags |
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| ### MeZO (v5.1 — Standard) |
| - Standard zeroth-order optimization |
| - 2 forward passes per step, no backward |
| - Good for fine-tuning; ~50-200 tok/s on CPU |
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| ### AdamW (v5.1 — Full backprop) |
| - Standard gradient descent with checkpointing |
| - Best convergence quality for pretraining from scratch |
| - ~10-50 tok/s on CPU |
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| --- |
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| ## References |
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| 37 papers indexed in `config.json` under `§`. Key additions for v5.3: |
| - [GrowLength](https://arxiv.org/abs/2310.00576) — Progressive sequence length training |
| - [GRC MatMul-free LM](https://arxiv.org/abs/2512.23145) — Reservoir computing for LMs |
| - [Sparse MeZO](https://arxiv.org/abs/2406.02913) — Sparse zeroth-order fine-tuning |
| - [GaLore](https://arxiv.org/abs/2403.03507) — Gradient low-rank projection |
| - [QuZO](https://arxiv.org/abs/2502.12346) — Quantized zeroth-order training |
| - [SparAMX](https://arxiv.org/abs/2502.12444) — AMX-accelerated sparse CPU kernels |
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| Plus all previous references: |
| - [Gated DeltaNet](https://arxiv.org/abs/2412.06464) — NVIDIA |
| - [xLSTM](https://arxiv.org/abs/2405.04517) — NXAI/JKU |
| - [Titans](https://arxiv.org/abs/2501.00663) — Google |
| - [Parcae](https://arxiv.org/abs/2604.12946) — Stanford/Together |
| - [BitNet b1.58](https://arxiv.org/abs/2402.17764) — Microsoft |
| - [MeZO](https://arxiv.org/abs/2305.17333) — Princeton |
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