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Closes the local-learning loop: a CPU/MI300X training run produces a
PEFT adapter under `<run_dir>/checkpoint/`; this module merges it into
the base weights, writes a Modelfile, and calls `ollama create` so the
tag is immediately servable on the same host the operator runs on.
Ollama 0.13+ accepts `FROM <safetensors-directory>` natively for Llama,
Mistral, Gemma, Qwen, and Phi architectures — so we don't need
llama.cpp for the supported families that mindXtrain trains. The merged
HF directory is the canonical artefact; ollama internalizes it on
`create`.
Lazy-imports `peft` / `transformers` so `import mindxtrain.deploy` stays
cheap on the CPU-only base install. Callers must opt into `--extra ml`.
"""
from __future__ import annotations
import shutil
import subprocess
from collections.abc import Callable
from dataclasses import dataclass
from pathlib import Path
@dataclass(frozen=True)
class OllamaPushResult:
tag: str
merged_dir: Path
modelfile: Path
ollama_stdout: str
# When push_to_ollama was called with register_with_mindx=True and
# the PATCH succeeded, this holds the mindX response
# ({previous, current, ...}). None when registration was skipped or
# failed (failure logs through `sink` but does not abort the push —
# the merged + tagged artefact still lands locally).
mindx_fallback_swap: dict[str, str] | None = None
def merge_lora_adapter(
base_model: str,
adapter_dir: Path,
out_dir: Path,
*,
sink: Callable[[str], None] | None = None,
) -> Path:
"""Merge a PEFT LoRA adapter into the base weights.
Writes the resulting full-precision HF directory to `out_dir`. The
directory is suitable as the `FROM` target of an ollama Modelfile.
Raises ImportError if the `ml` extras aren't installed; the message
points the user at the exact `uv sync` command.
"""
_emit = sink or (lambda _line: None)
try:
from peft import PeftModel # type: ignore
from transformers import AutoModelForCausalLM, AutoTokenizer # type: ignore
except ImportError as exc:
msg = (
"push-to-ollama needs `peft` + `transformers`. install with: "
"uv sync --extra ml"
)
raise ImportError(msg) from exc
_emit(f"[push-ollama] loading base model: {base_model}")
base = AutoModelForCausalLM.from_pretrained(base_model)
_emit(f"[push-ollama] applying adapter from {adapter_dir}")
merged = PeftModel.from_pretrained(base, str(adapter_dir)).merge_and_unload()
out_dir.mkdir(parents=True, exist_ok=True)
_emit(f"[push-ollama] saving merged weights to {out_dir}")
merged.save_pretrained(str(out_dir), safe_serialization=True)
# Carry the tokenizer too — ollama needs it for chat templating.
tokenizer = AutoTokenizer.from_pretrained(str(adapter_dir))
tokenizer.save_pretrained(str(out_dir))
_emit(f"[push-ollama] merged dir ready: {out_dir}")
return out_dir
def write_modelfile(
merged_dir: Path,
out_path: Path,
*,
system_prompt: str | None = None,
template: str | None = None,
parameters: dict[str, float | int | str] | None = None,
) -> Path:
"""Write an ollama Modelfile pointing at `merged_dir`.
Minimal contract: `FROM <merged_dir>` plus optional SYSTEM, TEMPLATE,
and PARAMETER stanzas. Ollama's docs are at
https://github.com/ollama/ollama/blob/main/docs/modelfile.md — the
safetensors-directory path is what ollama 0.13+ resolves natively.
"""
lines: list[str] = [f"FROM {merged_dir}"]
if system_prompt:
# SYSTEM blocks support triple-quoted multiline payloads.
lines.append(f'SYSTEM """{system_prompt}"""')
if template:
lines.append(f'TEMPLATE """{template}"""')
for key, value in (parameters or {}).items():
# Strings need quoting; numbers don't.
rendered = f'"{value}"' if isinstance(value, str) else value
lines.append(f"PARAMETER {key} {rendered}")
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
return out_path
def ollama_create(
tag: str,
modelfile: Path,
*,
sink: Callable[[str], None] | None = None,
ollama_bin: str | None = None,
timeout_s: float = 1800.0,
) -> str:
"""Run `ollama create <tag> -f <modelfile>` and return its stdout.
Raises FileNotFoundError if the ollama CLI isn't on PATH and no
explicit `ollama_bin` is provided. Raises CalledProcessError if
ollama returns non-zero (caller surfaces stderr to the user).
"""
_emit = sink or (lambda _line: None)
binary = ollama_bin or shutil.which("ollama")
if not binary:
msg = (
"`ollama` CLI not found on PATH. install from https://ollama.com "
"or pass --ollama-bin to point at a custom build"
)
raise FileNotFoundError(msg)
cmd = [binary, "create", tag, "-f", str(modelfile)]
_emit(f"[push-ollama] $ {' '.join(cmd)}")
result = subprocess.run(
cmd,
check=True,
capture_output=True,
text=True,
timeout=timeout_s,
)
if result.stdout:
_emit(result.stdout.rstrip())
if result.stderr:
# Ollama prints progress to stderr — surface it for visibility.
_emit(result.stderr.rstrip())
return result.stdout
def push_to_ollama(
base_model: str,
adapter_dir: Path,
tag: str,
*,
work_dir: Path | None = None,
system_prompt: str | None = None,
template: str | None = None,
parameters: dict[str, float | int | str] | None = None,
sink: Callable[[str], None] | None = None,
ollama_bin: str | None = None,
register_with_mindx: bool = False,
mindx_base_url: str | None = None,
) -> OllamaPushResult:
"""End-to-end: merge LoRA → Modelfile → ollama create.
`work_dir` defaults to `<adapter_dir>/../ollama_push/`. The merged HF
directory lives at `work_dir/merged/`; the Modelfile at
`work_dir/Modelfile`. Both are kept around so the operator can re-run
`ollama create` without redoing the merge.
When `register_with_mindx=True`, after a successful `ollama create`
we PATCH `<mindx_base_url>/v1/config/fallback-model` with
`{provider: "ollama", model: <tag>}` so the freshly pushed tag
becomes mindX's local fallback model. The PATCH is best-effort —
a failure logs through `sink` and lands in `OllamaPushResult` as
`mindx_fallback_swap=None`, but does NOT raise. The point of this
flag is "close the dream → train → fallback loop without manual
intervention"; a stopped mindX daemon shouldn't kill the push.
"""
work = work_dir or adapter_dir.parent / "ollama_push"
merged_dir = merge_lora_adapter(
base_model, adapter_dir, work / "merged", sink=sink,
)
modelfile = write_modelfile(
merged_dir, work / "Modelfile",
system_prompt=system_prompt, template=template, parameters=parameters,
)
stdout = ollama_create(tag, modelfile, sink=sink, ollama_bin=ollama_bin)
swap_result: dict[str, str] | None = None
if register_with_mindx:
_emit = sink or (lambda _line: None)
try:
# Lazy-imported so the deploy module stays importable on
# hosts without the publish-side dep tree warmed up.
from mindxtrain.deploy.api_client import swap_mindx_fallback_model
_emit(f"[push-ollama] registering {tag} with mindX as fallback")
swap_result = swap_mindx_fallback_model(
provider="ollama", model=tag, api_url=mindx_base_url,
)
_emit(
f"[push-ollama] mindX swap: "
f"{swap_result.get('previous', '?')} -> "
f"{swap_result.get('current', '?')}",
)
except Exception as exc:
# Swallow — see docstring rationale.
_emit(f"[push-ollama] mindX registration failed (push still ok): {exc}")
swap_result = None
return OllamaPushResult(
tag=tag, merged_dir=merged_dir, modelfile=modelfile,
ollama_stdout=stdout, mindx_fallback_swap=swap_result,
)
__all__ = [
"OllamaPushResult",
"merge_lora_adapter",
"ollama_create",
"push_to_ollama",
"write_modelfile",
]
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