# PhysisML in ollama.
#
# This file is language-neutral: `scripts/export_hf.py --lang <lang> --gguf ...`
# copies it beside the GGUF it ships and rewrites the FROM line. Build the GGUF
# from the last level of the language you want:
#
#   python3 scripts/export_gguf.py \
#       --checkpoint models/checkpoints/<lang>/level_<N>/final_dreamed.pt \
#       --tokenizer  models/checkpoints/<lang>/level_<N>/tokenizer.json \
#       --output     models/physisml.gguf
#   ollama create physisml -f Modelfile && ollama run physisml
#
# TEMPLATE is the bare prompt: the model was taught single-turn question/answer
# lines, so any chat wrapper is out of distribution. Greedy because the margin
# is thin and the published scores are measured that way.
#
# No stop strings: the model emits <|EOS|> on its own since the corpus started
# carrying it (P(EOS) after a finished answer went from 0.00004 to 0.85-1.00).
# It used to need `PARAMETER stop . ! ?` , which also ate the final punctuation.
#
# `ollama run` INTERACTIVELY sends the accumulated conversation back as context.
# With a 128-token window and no multi-turn training, three exchanges are enough
# to crowd out the question: the model answers the tail of the transcript rather
# than the question that opened it. Use /clear, one-shot
# `ollama run physisml "..."`, or the API — all three are stateless.
FROM ./physisml.gguf
TEMPLATE {{ .Prompt }}
PARAMETER temperature 0
PARAMETER top_k 1
PARAMETER num_predict 48
PARAMETER num_ctx 128
PARAMETER repeat_penalty 1