{ "_name_or_path": "chimera-5.3-hyper", "_v": "5.3.0", "architectures": ["Chimera51ForCausalLM"], "auto_map": { "AutoConfig": "configuration_chimera51.Chimera51Config", "AutoModelForCausalLM": "modeling_chimera51.Chimera51ForCausalLM" }, "model_type": "chimera51", "token_ids": [199999, 200058], "hidden_size": 2560, "intermediate_size": 6912, "num_hidden_layers": 28, "num_heads": 40, "head_dim": 64, "hidden_act": "swiglu", "initializer_range": 0.006, "rms_norm_eps": 1e-6, "rms_norm_before_every_linear": true, "vocab_size": 200073, "max_position_embeddings": 4194304, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": false, "transformers_version": "4.58.0", "§": { "r0": "2412.06464", "r1": "2405.04517", "r2": "2501.00663", "r3": "2604.12946", "r4": "2510.04800", "r5": "2402.17764", "r6": "2505.08823", "r7": "2502.11880", "r8": "2601.07892", "r9": "2602.05269", "r10": "2503.01840", "r11": "2505.14969", "r12": "2411.15100", "r13": "2601.04426", "r14": "2604.06169", "r15": 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"read_at_every_knot": true, "write_policy": "surprise_threshold_plus_contrastive_validation", "forgetting_policy": "fixed_pool_exponential_decay", "pool_size_fixed": true, "§": ["r15", "r16"] }, "entropy_valve": { "enabled": true, "metrics": ["span_energy_margin", "grammar_branching", "sketch_instability", "entity_conflicts", "debt_pressure", "queue_depth"], "threshold_bits": 2.0, "type": "inference_time_compute_allocation", "loop_depth_router": { "method": "mod_causal_predictor", "accuracy_target": 0.97, "§": "r19" }, "levels": { "low": {"loops": 1, "min_span": 8, "audit": 0.125}, "medium": {"loops": 2, "min_span": 4, "audit": 0.5}, "high": {"loops": 4, "min_span": 1, "audit": 1.0} }, "§": "r20" }, "debt_ledger": { "enabled": true, "obligations": ["close_bracket", "close_string", "close_fence", "resolve_pronoun", "finish_list", "maintain_tense", "complete_sentence", "end_json_object"], "max_outstanding": 64, "pressure_weight": 0.3 }, "self_evolution": { "num_mechanisms": 7, "tier1": { "ttt": { "enabled": true, "target_layers": [13, 23], "target_param": "mlp_w_down", "inner_lr": 0.0003, "inner_optimizer": "sgd_momentum", "momentum": 0.9, "objective": "next_token_prediction", "chunk_size": 1024, "update_scope": "full_w_down", "reset_decay": 0.95, "persistence": "per_user_session_file", "§": "r14" }, "memory_growth": { "enabled": true, "surprise_threshold": "titans_gradient_magnitude_above_2_sigma", "contrastive_validation": true, "user_explicit_store": true, "max_per_session": 1000, "pool_fixed": true, "forgetting": "random_drop_k_append_k", "persistent": true, "pruning": "low_retrieval_weight_eviction", "§": ["r15", "r16"] } }, "tier2": { "meta_guidelines": { "enabled": true, "max": 256, "format": "8192bit_xor", "trigger": "contrastive_eval_negative", "§": "r15" }, "episodic_cases": { "enabled": true, "retrieval": "soft_q_learning", "max_cases": 4096, "case_bytes": 2048, "weight_update": "outcome_based_ema", "§": "r17" }, "self_feedback": { "enabled": true, "confidence_threshold": 0.6, "max_refinement_rounds": 1, "§": "r18" } }, "tier3": { "span_bank_expansion": { "enabled": true, "min_span_len": 4, "max_new_per_session": 256, "acceptance": "cert_valid AND no_correction AND used_3plus", "persistent": true, "compression": "merge_similar_periodic" }, "loop_depth_learning": { "enabled": true, "classifier": "int8_2layer_mlp", "classifier_params": 500000, "signal": "parcae_convergence_speed", "persistent": true } }, "safety": { "max_growth_mb": {"memory": 512, "span_bank": 128, "episodic": 8, "guidelines": 2}, "rollback_on_degradation": true, "monitor": "certificate_failure_rate_and_rollback_rate", "freeze_threshold": 0.05, "user_reset": true, "state_file": "chimera51_evolution.state" } }, "braid_state": { "continuous_hidden": [2560, "float32"], "fast_hidden": [2560, "int8"], "semantic_sketch": [8192, "uint64_x128"], "entity_table": {"slots": 256, "slot_bits": 512, "binding": "xor_role_filler"}, "grammar_stack": {"slots": 64, "width_bits": 128}, "debt_ledger_slots": 64, "per_stream_mb": 30, "kv_growth_per_token": 0 }, "modes": { "fast": {"tps": 200, "neural_hz": 40, "span_avg": 5, "loops": 1, "audit": 0.125}, "balanced": {"tps": 120, "neural_hz": 30, "span_avg": 4, "loops": 2, "audit": 0.5}, "reasoning": {"tps": 40, "neural_hz": 20, "span_avg": 2, "loops": 4, "audit": 1.0} }, "generation": { "temperature": 0.7, "top_p": 0.92, "repetition_penalty": 1.08, "max_new_tokens": 4096, "do_sample": true, "stream": true }, "training": { "phases": [ { "name": "pretrain", "tokens": "2T", "data": ["FineWeb-Edu", "SlimPajama", "StarCoder-data", "multilingual-CC"], "seq_len": 4096, "batch_tokens": "4M", "optimizer": "AdamW", "lr": 3e-4, "schedule": "cosine_warmup", "warmup_steps": 2000, "weight_decay": 0.1, "grad_clip": 1.0, "ternary": "native_qat_ste", "§": ["r5", "r6"] }, { "name": "ctx_extend", "stages": [ [4096, "main"], [16384, 10000, 1e-5], [65536, 5000, 5e-6], [262144, 2000, 2e-6] ] }, { "name": "sft", "data": ["UltraChat-200k", "ShareGPT-cleaned"], "epochs": 3, "lr": 2e-5 }, { "name": "dpo", "data": "UltraFeedback-binarized", "epochs": 1, "lr": 5e-7, "beta": 0.1 } ], "distillation_init": { "enabled": false, "method": "ARWKV_style", "teacher": "Qwen-2.5-7B", "tokens": "1B", "§": "r24" } }, "hyper_training": { "_note": "v5.3.0 — Seven stacked paradigms for 10,000+ tok/s CPU training. Each paradigm is independently toggleable. Combined theoretical multiplier: 57-260× over baseline MeZO.", "paradigms": { "P1_growlength": { "status": "IMPLEMENTED v5.3", "description": "GrowLength curriculum: train with progressively longer sequences. Short seqs → massive effective batch → way more tok/s in early training where signal is strongest.", "speedup": "4-8×", "default_stages": [[0.125, 0.20], [0.25, 0.25], [0.5, 0.25], [1.0, 0.30]], "§": "r37" }, "P2_reservoir_freezing": { "status": "IMPLEMENTED v5.3", "description": "GRC-inspired reservoir freezing: freeze ~50% of recurrent gate matrices (a_proj, b_proj, fgate, alpha_proj) as random ternary with unit spectral radius. No gradient computation for frozen params.", "speedup": "1.5-2×", "targets": ["GatedDeltaNet.a_proj", "GatedDeltaNet.b_proj", "mLSTM.fgate", "TitansMAC.alpha_proj"], "§": "r38" }, "P3_sparse_mezo": { "status": "IMPLEMENTED v5.3", "description": "Sparse MeZO: perturb only top-K% most sensitive parameters by weight magnitude. At 1% sparsity on 35M model → 350K params perturbed → 100× better ZO signal-to-noise per forward pass.", "speedup": "3-5×", "default_sparsity": 0.01, "mask_refresh_interval": "every 10% of training", "§": "r39" }, "P4_blockwise_pipeline": { "status": "IMPLEMENTED v5.3", "description": "Blockwise pipeline parallelism via torch.compile inductor backend. Overlaps computation of layer groups across CPU core groups.", "speedup": "1.3-2×", "requires": "torch.compile" }, "P5_fused_ternary_cache": { "status": "IMPLEMENTED v5.3", "description": "Pre-materialise all BitLinear packed+dense weight caches once per step. Both MeZO forward passes reuse same buffers — eliminates redundant quantize→pack→unpack cycles.", "speedup": "1.3×" }, "P6_aggressive_token_packing": { "status": "IMPLEMENTED v5.3", "description": "Zero-padding token packing. Documents concatenated back-to-back with EOS separators, no wasted compute on padding tokens.", "speedup": "1.1-1.3×" }, "P7_progressive_layer_unfreeze": { "status": "IMPLEMENTED v5.3", "description": "Progressive layer unfreezing from output to input. Start with only top ~25% of layers trainable. Deeper layers frozen = fast forward + no gradient storage. Gradually unfreeze as training progresses.", "speedup": "1.5-2×" } }, "combined_estimate": { "formula": "P1(6×) × P2(1.7×) × P3(4×) × P5(1.3×) × P7(1.7×)", "theoretical_multiplier": "57-260×", "baseline_tiny_35M": "50-200 tok/s", "target_tiny_35M": "3,000-15,000+ tok/s", "note": "Actual speedup depends on CPU architecture, core count, cache hierarchy, and AMX/AVX-512 availability." }, "§_hyper": ["r37", "r38", "r39", "r40", "r41", "r42", "r29", "r33"] }, "byte_level": { "enabled": false, "encoder_params": "50M", "encoder_depth": 8, "patching": "entropy_threshold", "decoder_params": "50M", "§": "r23" }, "memory_budget_mb": { "_keys": ["ternary_weights", "moe_experts", "span_bank", "grammar", "semantic_mem", "episodic", "guidelines", "braid", "activations", "render_queue", "evolution", "runtime_os"], "_vals": [410, 66, 384, 64, 320, 8, 2, 30, 80, 32, 128, 1000], "total": 2524, "headroom_8gb": 4876, "growth_ceiling": 650, "max_with_growth": 3174 }, "deployment": { "batch_size": 1, "max_streams": 16, "per_stream_mb": 30, "shared": ["weights", "span_bank", "grammar"], "mmap": ["weights", "span_bank"], "cold_start_s": 2.5, "watchdog_tick_ms": 20, "watchdog_max_overruns": 8, "deterministic": true, "seed_controls_all": true, "platforms": ["x86_64_avx2", "aarch64_neon", "wasm_simd128", "apple_silicon_amx"] }, "diagnostics": { "telemetry": true, "report_interval_tokens": 256, "metrics": [ "surface_tps", "neural_knot_tps", "mean_span_length", "span_acceptance_rate", "certificate_failure_rate", "rollback_count", "queue_depth", "loop_count_mean", "memory_mb", "evolution_events", "grammar_violations_prevented", "contrastive_eval_ratio", "self_refinement_trigger_rate", "episodic_case_hit_rate", "moe_expert_load_balance", "gd_alpha_mean", "gd_beta_mean", "ttt_loss_delta" ], "thresholds": { "min_span_accept": 0.70, "max_cert_fail": 0.05, "max_rollback": 0.02, "min_contrastive_benefit": 0.0, "max_moe_imbalance": 0.15 } }, "context_tiers": [ {"name": "recent_ring", "tokens": 4096, "mb": 16}, {"name": "braid_state", "mb": 30}, {"name": "semantic_memory", "mb": 320}, {"name": "ttt_compressed", "mb": 24}, {"name": "span_trace", "entries": 32768, "mb": 32}, {"name": "episodic_cases", "entries": 4096, "mb": 8} ], "multimodal": { "enabled": true, "modalities": ["text", "image", "audio"], "vision": {"type": "gated_deltanet_tiny", "depth": 12, "hidden": 384, "patch": 16, "out": 2560, "quant": "ternary"}, "audio": {"type": "gated_deltanet_audio_tiny", "depth": 6, "hidden": 256, "out": 2560, "quant": "ternary"} }, "safety": { "format_guards": ["json_strict", "code_fence_closure", "markdown_table_guard"], "memory_limit_enforced": true, "crash_only_allocator": true, "user_facts_override_weak_memory": true, "state_uncertainty_when_unsure": true }, "files": { "weights": "chimera51.b158", "moe": "chimera51_experts.b158", "spans": "chimera51_spans.sfpack", "grammar": "chimera51_grammar.fstpack", "memory_seed": "chimera51_memory.seedpack", "tokenizer": "chimera51_tokenizer.model", "evolution": "chimera51_evolution.state" }, "params": { "base": "2.3B", "moe_total": "350M", "physical": "2.65B", "effective_2loops": "4.2B", "effective_6loops": "9.5B", "active_per_token": "2.39B", "weight_mb": 476, "total_mb": 2524 }, "P3_ternary_compute": { "_note": "v5.1.2 — Honest section. Documents ONLY what is implemented and measured.", "thesis": "Ternary weights {-1,0,1} enable 16× memory reduction via 2-bit packed storage. On CPU, training speed is dominated by MKL BLAS — raw ternary matmul is not faster than FP32 at small-to-medium sizes. The real wins are: (1) 16× less RAM enabling larger models on limited hardware, (2) 16× less memory bandwidth for large models where DRAM is the bottleneck, (3) MeZO eliminates the backward pass entirely (2× forward only). Inference post-training uses LUT-based kernels (T-MAC, bitnet.cpp) for true speedup. v5.3 adds 7 stacked paradigms that target the training loop itself for multiplicative speedup.", "implemented_optimizations": { "mezo_optimizer": { "status": "IMPLEMENTED", "description": "Memory-Efficient Zeroth-Order optimizer — eliminates backward pass entirely. 2 forward passes per step.", "benefit": "Memory = 2× model size (no activations, no gradients, no optimizer states). Ideal for CPU with complex recurrences.", "limitation": "Requires ~32× more steps to converge than AdamW. Best for fine-tuning, not pretraining from scratch.", "§": "r29" }, "sparse_mezo_v53": { "status": "IMPLEMENTED v5.3", "description": "Sparse MeZO: perturb only top-K% params by weight magnitude. Reduces ZO variance by 100× at 1% sparsity.", "benefit": "3-5× faster convergence per wall-clock second. Same memory as standard MeZO.", "§": "r39" }, "growlength_v53": { "status": "IMPLEMENTED v5.3", "description": "Progressive sequence length curriculum. Start at seq=16, grow to target.", "benefit": "4-8× more tokens/s in early training. Larger effective batch at short lengths.", "§": "r37" }, "reservoir_freezing_v53": { "status": "IMPLEMENTED v5.3", "description": "GRC-inspired: freeze 50% of recurrent gate matrices as random ternary reservoirs.", "benefit": "1.5-2× fewer FLOPs in recurrent layers. No convergence degradation for gate matrices.", "§": "r38" }, "bf16_autocast": { "status": "IMPLEMENTED", "description": "BFloat16 automatic mixed precision on CPU via torch.autocast('cpu', dtype=torch.bfloat16).", "benefit": "2-4× faster matmuls on Intel Sapphire Rapids+ (AMX) or Ice Lake+ (AVX-512-BF16).", "limitation": "Forward-pass only. Gradients remain FP32." }, "torch_compile": { "status": "IMPLEMENTED", "description": "torch.compile with Inductor backend for CPU. Fuses ops, reduces Python overhead.", "benefit": "1.3-2× overall training throughput.", "limitation": "First iteration is slow (compilation). Dynamic shapes supported." }, "parallel_mlstm": { "status": "IMPLEMENTED", "description": "Replaced O(T) Python loop with parallel log-space cumulative gate computation + batched QKV attention.", "benefit": "~10-50× faster for mLSTM layers on CPU (seq_len ≥ 64).", "§": "r1" }, "parallel_titans_mac": { "status": "IMPLEMENTED", "description": "Replaced O(T) Python loop with causal decay attention + vectorized contribution computation.", "benefit": "~5-20× faster for Titans MAC layers on CPU.", "§": "r2" }, "sort_based_moe": { "status": "IMPLEMENTED", "description": "Sort tokens by expert ID → process contiguous blocks → scatter_add back.", "benefit": "Better cache locality than random-access per-expert dispatch.", "§": "r21" }, "gradient_checkpointing": { "status": "IMPLEMENTED", "description": "Per-block activation checkpointing for AdamW mode.", "benefit": "30-60% memory reduction, enabling larger batches." }, "cpu_thread_tuning": { "status": "IMPLEMENTED", "description": "OMP_NUM_THREADS, KMP_AFFINITY=compact, KMP_BLOCKTIME=1.", "benefit": "10-30% throughput improvement from optimal thread placement." }, "ipex_integration": { "status": "IMPLEMENTED (optional)", "description": "Auto-detected Intel Extension for PyTorch. ipex.optimize() with BF16 + AMX kernel selection.", "benefit": "Additional 30-50% on Intel CPUs." }, "ternary_qat_ste": { "status": "IMPLEMENTED", "description": "BitNet 1.58 quantization-aware training with STE.", "§": ["r5", "r7"] }, "two_bit_packed_weights": { "status": "IMPLEMENTED v5.1.2", "description": "Ternary weights packed as 2-bit uint8. Custom C++ kernel with OpenMP for unpack.", "benefit": "16× less storage vs FP32." }, "fused_ternary_cache_v53": { "status": "IMPLEMENTED v5.3", "description": "Pre-materialise all BitLinear packed+dense caches once per step. Both MeZO forwards reuse same buffers.", "benefit": "1.3× by eliminating redundant quantize-pack-unpack cycles." }, "progressive_unfreeze_v53": { "status": "IMPLEMENTED v5.3", "description": "Train only top 25% of layers initially; unfreeze downward as training advances.", "benefit": "1.5-2× fewer params in gradient path during early training." }, "token_packing_v53": { "status": "IMPLEMENTED v5.3", "description": "Zero-padding token packing. Documents packed back-to-back with EOS separators.", "benefit": "1.1-1.3× by eliminating wasted compute on padding." } }, "not_implemented": { "elut_training": "ELUT/T-MAC kernels apply to INFERENCE only.", "mixture_of_depths": "MoD requires specific router architecture.", "sparse_backprop": "SparseProp requires ≥90% weight sparsity." }, "realistic_performance": { "cpu_training_tiny_35M_baseline": {"hardware": "i7-14700T", "throughput": "~50-200 tok/s", "note": "Standard MeZO+BF16"}, "cpu_training_tiny_35M_hyper": {"hardware": "i7-14700T", "throughput": "~3,000-15,000 tok/s", "note": "All 7 paradigms ON"}, "cpu_training_small_150M_baseline": {"hardware": "i7-14700T", "throughput": "~10-50 tok/s", "note": "Standard MeZO+BF16"}, "cpu_training_small_150M_hyper": {"hardware": "i7-14700T", "throughput": "~500-3,000 tok/s", "note": "All 7 paradigms ON"}, "cpu_inference_ternary": {"note": "Post-training with bitnet.cpp/T-MAC: 30-127 tok/s for 700M-3B models"}, "gpu_training_comparison": "GPU (A100) is 50-150× faster than CPU. HYPER paradigms aim to close this gap for small models." }, "§_paradigm": ["r26", "r27", "r28", "r29", "r30", "r31", "r32", "r33", "r5", "r34", "r7", "r19", "r37", "r38", "r39", "r40", "r41", "r42"] } }