# Model registry. Spec section 4.4: the filtering models must live in # configuration rather than being hard-coded. # # The evaluated set is 5 families x 4 models: four size tiers, and four clean # base<->instruct pairs spread across four different families. # # WEIGHTS. Each entry carries an `hf:` hub id, which is what the runner uses by # default. To evaluate a model that is not listed here, either add an entry or # pass `--model-path ` to runner/eval_run.py. To use a local # mirror of the listed models, set FKS_MODELS to a directory whose subdirectory # names match each entry's `path:`, or uncomment `model_root` below. # # model_root: /path/to/your/weights # # `n_layers`, `d_model` and `jlens_window` are recorded for cross-model # analysis; jlens_window is a redundant assertion checked at extraction time # against layer_window() -- the binding definition is d_l = l/(L-1) >= 0.4 # (protocol 7.10), not this number. # ---- Stage I: five-model first-token candidate recall ----------------------- # FROZEN. These five defined the 8,107-fact candidate pool that everything # downstream rests on. Changing this list invalidates # candidate_known_8107.jsonl and every artefact derived from it, so it must stay # fixed even as the evaluated set grows. filter_models: - {name: Gemma-2-2B-it, path: Gemma-2-2B-it, revision: main} - {name: Qwen2.5-7B, path: Qwen2.5-7B, revision: main} - {name: Mistral-7B-v0.3, path: Mistral-7B-v0.3, revision: main} - {name: Llama-3.1-8B, path: Llama-3.1-8B, revision: main} - {name: Gemma-2-9B-it, path: Gemma-2-9B-it, revision: main} # ---- Stage V/VI: the 20 evaluated models ------------------------------------ # Spec 7.1: qualification produces K_m LABELS only. Adding or removing a model # here never changes the benchmark denominator (fixed at 2,592), so this list is # safe to extend at any time without invalidating earlier results. # # n_layers / d_model / jlens_window come from MODEL_SELECTION_20.md, where # window = number of layers with normalized depth >= 0.4. evaluated_models: # ---------------- Llama-3.x ---------------- - {name: Llama-3.2-1B, path: Llama-3.2-1B, hf: meta-llama/Llama-3.2-1B, family: llama-3.x, tier: 1-5B, params_b: 1.24, tuning: base, n_layers: 16, d_model: 2048, jlens_window: 10, revision: main} - {name: Llama-3.2-3B, path: Llama-3.2-3B, hf: meta-llama/Llama-3.2-3B, family: llama-3.x, tier: 1-5B, params_b: 3.21, tuning: base, n_layers: 28, d_model: 3072, jlens_window: 17, revision: main} - {name: Llama-3.1-8B, path: Llama-3.1-8B, hf: meta-llama/Llama-3.1-8B, family: llama-3.x, tier: 5-9B, params_b: 8.03, tuning: base, n_layers: 32, d_model: 4096, jlens_window: 19, revision: main, pair: llama_8b} - {name: Llama-3.1-8B-Instruct, path: Llama-3.1-8B-Instruct, hf: meta-llama/Llama-3.1-8B-Instruct, family: llama-3.x, tier: 5-9B, params_b: 8.03, tuning: instruct, n_layers: 32, d_model: 4096, jlens_window: 19, revision: main, pair: llama_8b} # ---------------- Qwen2.5 ---------------- - {name: Qwen2.5-3B, path: Qwen2.5-3B, hf: Qwen/Qwen2.5-3B, family: qwen2.5, tier: 1-5B, params_b: 3.09, tuning: base, n_layers: 36, d_model: 2048, jlens_window: 22, revision: main} - {name: Qwen2.5-7B, path: Qwen2.5-7B, hf: Qwen/Qwen2.5-7B, family: qwen2.5, tier: 5-9B, params_b: 7.62, tuning: base, n_layers: 28, d_model: 3584, jlens_window: 17, revision: main, pair: qwen_7b} - {name: Qwen2.5-7B-Instruct, path: Qwen2.5-7B-Instruct, hf: Qwen/Qwen2.5-7B-Instruct, family: qwen2.5, tier: 5-9B, params_b: 7.62, tuning: instruct, n_layers: 28, d_model: 3584, jlens_window: 17, revision: main, pair: qwen_7b} - {name: Qwen2.5-32B, path: Qwen2.5-32B, hf: Qwen/Qwen2.5-32B, family: qwen2.5, tier: 20B+, params_b: 32.76, tuning: base, n_layers: 64, d_model: 5120, jlens_window: 38, revision: main} # ---------------- Mistral ---------------- - {name: Mistral-7B-v0.3, path: Mistral-7B-v0.3, hf: mistralai/Mistral-7B-v0.3, family: mistral, tier: 5-9B, params_b: 7.25, tuning: base, n_layers: 32, d_model: 4096, jlens_window: 19, revision: main, pair: mistral_7b} - {name: Mistral-7B-Instruct-v0.3, path: Mistral-7B-Instruct-v0.3, hf: mistralai/Mistral-7B-Instruct-v0.3, family: mistral, tier: 5-9B, params_b: 7.25, tuning: instruct, n_layers: 32, d_model: 4096, jlens_window: 19, revision: main, pair: mistral_7b} - {name: Mistral-Nemo-Base-2407, path: Mistral-Nemo-Base-2407, hf: mistralai/Mistral-Nemo-Base-2407, family: mistral, tier: 9-20B, params_b: 12.25, tuning: base, n_layers: 40, d_model: 5120, jlens_window: 24, revision: main} - {name: Mistral-Small-24B-Base-2501, path: Mistral-Small-24B-Base-2501, hf: mistralai/Mistral-Small-24B-Base-2501, family: mistral, tier: 20B+, params_b: 23.57, tuning: base, n_layers: 40, d_model: 5120, jlens_window: 24, revision: main} # ---------------- Gemma-2 ---------------- # Gemma-2 and OLMo-2 publish fp32 safetensors only; they are loaded with # dtype=bfloat16, so on-disk size is ~2x the bf16 figure. - {name: gemma-2-2b, path: gemma-2-2b, hf: google/gemma-2-2b, family: gemma-2, tier: 1-5B, params_b: 2.61, tuning: base, n_layers: 26, d_model: 2304, jlens_window: 16, revision: main} - {name: gemma-2-9b, path: gemma-2-9b, hf: google/gemma-2-9b, family: gemma-2, tier: 9-20B, params_b: 9.24, tuning: base, n_layers: 42, d_model: 3584, jlens_window: 25, revision: main, pair: gemma_9b} - {name: Gemma-2-9B-it, path: Gemma-2-9B-it, hf: google/gemma-2-9b-it, family: gemma-2, tier: 9-20B, params_b: 9.24, tuning: instruct, n_layers: 42, d_model: 3584, jlens_window: 25, revision: main, pair: gemma_9b} - {name: gemma-2-27b, path: gemma-2-27b, hf: google/gemma-2-27b, family: gemma-2, tier: 20B+, params_b: 27.23, tuning: base, n_layers: 46, d_model: 4608, jlens_window: 28, revision: main} # ---------------- OLMo-2 ---------------- # The only family with all four tiers AND fully public pretraining data, which # is what makes "the model never saw this fact" separable from "the model # cannot retrieve it". - {name: OLMo-2-0425-1B, path: OLMo-2-0425-1B, hf: allenai/OLMo-2-0425-1B, family: olmo-2, tier: 1-5B, params_b: 1.48, tuning: base, n_layers: 16, d_model: 2048, jlens_window: 10, revision: main} - {name: OLMo-2-1124-7B, path: OLMo-2-1124-7B, hf: allenai/OLMo-2-1124-7B, family: olmo-2, tier: 5-9B, params_b: 7.30, tuning: base, n_layers: 32, d_model: 4096, jlens_window: 19, revision: main} - {name: OLMo-2-1124-13B, path: OLMo-2-1124-13B, hf: allenai/OLMo-2-1124-13B, family: olmo-2, tier: 9-20B, params_b: 13.72, tuning: base, n_layers: 40, d_model: 5120, jlens_window: 24, revision: main} - {name: OLMo-2-0325-32B, path: OLMo-2-0325-32B, hf: allenai/OLMo-2-0325-32B, family: olmo-2, tier: 20B+, params_b: 32.23, tuning: base, n_layers: 64, d_model: 5120, jlens_window: 38, revision: main} # ---- base <-> instruct pairs (MODEL_SELECTION_20.md section 4) -------------- # Same weights lineage, same tokenizer, same size; only post-training differs. # If BES improves while ISS does not, instruction tuning stabilised EXPRESSION # rather than KNOWLEDGE. tuning_pairs: llama_8b: {base: Llama-3.1-8B, instruct: Llama-3.1-8B-Instruct, family: llama-3.x, tier: 5-9B} qwen_7b: {base: Qwen2.5-7B, instruct: Qwen2.5-7B-Instruct, family: qwen2.5, tier: 5-9B} mistral_7b: {base: Mistral-7B-v0.3, instruct: Mistral-7B-Instruct-v0.3, family: mistral, tier: 5-9B} gemma_9b: {base: gemma-2-9b, instruct: Gemma-2-9B-it, family: gemma-2, tier: 9-20B} # ---- models that do NOT occupy an evaluated slot ---------------------------- auxiliary_models: stage_a_calibration: name: Qwen2.5-0.5B path: Qwen2.5-0.5B hf: Qwen/Qwen2.5-0.5B params_b: 0.49 n_layers: 24 d_model: 896 reason: exact full-matrix reference for J-Lens (J-Lens spec section 5) judge: name: Qwen2.5-32B-Instruct path: Qwen2.5-32B-Instruct hf: Qwen/Qwen2.5-32B-Instruct params_b: 32.76 n_layers: 64 d_model: 5120 reason: semantic clustering judge (evaluation protocol section 3) # Must be instruct (structured JSON output) and multilingual-strong: 12,010 # of 44,416 queries are zh/fr/es/de/ru. known_bias: > shares pretraining lineage with the four evaluated Qwen models and may systematically favour their phrasing. Mitigation: agreement check against src/scoring_full.py on a stratified sample, and optionally a second judge (gemma-2-27b-it) on a subsample. The judge is never itself evaluated. # ---- excluded, with reasons (MODEL_SELECTION_20.md section 9) --------------- excluded_models: - {name: Llama-3.1-70B, reason: "bf16 weights 141 GB vs 143 GB H200; quantisation would alter hidden states and break ISS/KTS comparability"} - {name: Qwen2.5-14B, reason: "could fill 9-20B but dropped to keep families balanced at 4 each; that tier still has 3 base models. Weights remain under Model/ but are not evaluated."} - {name: Llama-2-13B, reason: "different generation from Llama-3; mixing would break the family axis"} - {name: Phi-3/Phi-4, reason: "heavily synthetic training data; atypical knowledge profile"} - {name: Yi/InternLM/GLM, reason: "adds family breadth but no new scientific axis"} # ---- Stage V/VI generation configuration (spec section 7.3) ----------------- generation: do_sample: false num_beams: 1 temperature: 0.0 max_new_tokens: 24 max_prompt_len: 96 batch_size: 96 dtype: bfloat16 # Spec 7.2: identical raw prompt string for base and instruct models, so no # chat template is ever applied. This matters more with 4 instruct models in # the set: giving only those a chat template would confound tuning with format. use_chat_template: false seed: 20260101