dataset_upload/configs/models.yaml

# 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 <dir or hub id>` 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