# configs/lm_eval_compare_study.yaml
# ─────────────────────────────────────────────────────────────────────────────
# Shared settings for baseline vs finetuned comparisons.
# Use the SAME config for both runs; only change --preset / --experiment-name.
#
# Baseline:
#   uv run --package slm-evals slm-lm-eval \
#     --config research/evals/configs/lm_eval_compare_study.yaml \
#     --preset minicpm5-1b \
#     --experiment-name minicpm5-1b__baseline
#
# Candidate (after finetune):
#   uv run --package slm-evals slm-lm-eval \
#     --config research/evals/configs/lm_eval_compare_study.yaml \
#     --preset minicpm5-1b-lesson-lora \
#     --experiment-name minicpm5-1b-lora__v1 \
#     --compare-to results/lm_eval/minicpm5-1b__baseline/results.json
# ─────────────────────────────────────────────────────────────────────────────

study:
  baseline_preset: minicpm5-1b
  candidate_preset: minicpm5-1b-lesson-lora
  notes: >
    Keep tasks, num_fewshot, limit, and seed identical across runs.
    Do not compare training_results.json result_score to lm-eval accuracy.

tasks:
  - arc_easy
  - arc_challenge
  - hellaswag
  - piqa
  - boolq
  - gsm8k

num_fewshot: 5
limit: 100
seed: 42
batch_size: auto
device: auto
dtype: bfloat16
trust_remote_code: true
output_dir: results/lm_eval