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import json
from pathlib import Path
import modal
APP_NAME = "telltale-model-eval"
PROJECT_ROOT = None
def _find_project_root() -> Path:
current_file = Path(__file__).resolve()
for parent in current_file.parents:
if (parent / "telltale").is_dir():
return parent
return Path("/root")
PROJECT_ROOT = _find_project_root()
image = (
modal.Image.from_registry("nvidia/cuda:12.4.1-runtime-ubuntu22.04", add_python="3.11")
.apt_install("curl")
.env(
{
"HF_XET_HIGH_PERFORMANCE": "1",
"PYTHONPATH": "/root",
}
)
.pip_install("huggingface_hub[hf_transfer]", "pydantic", "numpy")
.run_commands(
"python -m pip install --upgrade pip",
"python -m pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124",
)
.add_local_dir(PROJECT_ROOT / "telltale", remote_path="/root/telltale")
)
app = modal.App(APP_NAME, image=image)
hf_cache = modal.Volume.from_name("telltale-hf-cache", create_if_missing=True)
@app.function(
gpu="L4",
cpu=4,
memory=24_576,
timeout=20 * 60,
secrets=[modal.Secret.from_name("huggingface-secret")],
volumes={"/root/.cache/huggingface": hf_cache},
)
def run_model_eval(
candidate_labels: list[str] | None = None,
candidate_file_json: str | None = None,
case_ids: list[str] | None = None,
max_cases: int | None = 3,
max_tokens: int = 320,
context_size: int = 2048,
temperature: float = 0.30,
seed: int = 17,
n_gpu_layers: int = -1,
speech_max_words: int = 16,
rationale_max_words: int = 24,
) -> str:
from telltale.models.eval_prompts import (
COMPARISON_MODEL_CANDIDATES,
DEFAULT_MODEL_CANDIDATES,
EVAL_CASES_BY_ID,
MODEL_CANDIDATES_BY_LABEL,
ModelCandidate,
get_eval_case,
)
from telltale.models.eval_runner import (
EvalRunConfig,
build_runtime_for_candidate,
evaluate_candidate,
write_eval_bundle,
)
candidates_by_label = dict(MODEL_CANDIDATES_BY_LABEL)
if candidate_file_json:
data = json.loads(candidate_file_json)
if isinstance(data, dict):
data = data.get("candidates", [])
for item in data:
candidate = ModelCandidate.from_mapping(item)
candidates_by_label[candidate.label] = candidate
if candidate_labels and "all" in candidate_labels:
selected_candidates = list(COMPARISON_MODEL_CANDIDATES)
elif candidate_labels:
selected_candidates = []
for label in candidate_labels:
if label not in candidates_by_label:
known = ", ".join(sorted(candidates_by_label))
raise ValueError(f"unknown candidate {label!r}; known candidates: {known}")
selected_candidates.append(candidates_by_label[label])
else:
selected_candidates = list(DEFAULT_MODEL_CANDIDATES)
if case_ids:
selected_cases = tuple(get_eval_case(case_id) for case_id in case_ids)
else:
selected_cases = tuple(EVAL_CASES_BY_ID.values())
if max_cases is not None:
selected_cases = selected_cases[:max_cases]
config = EvalRunConfig(
context_size=context_size,
max_tokens=max_tokens,
temperature=temperature,
seed=seed,
output_dir="/tmp/telltale_model_evals",
hardware_profile="modal_l4",
n_gpu_layers=n_gpu_layers,
speech_max_words=speech_max_words,
rationale_max_words=rationale_max_words,
)
results_by_candidate = {}
for candidate in selected_candidates:
runtime = build_runtime_for_candidate(candidate, config)
results_by_candidate[candidate.label] = evaluate_candidate(
candidate,
runtime,
cases=selected_cases,
config=config,
)
bundle = write_eval_bundle(results_by_candidate, output_dir=config.output_dir)
return json.dumps(
{
"candidate_labels": [candidate.label for candidate in selected_candidates],
"case_ids": [case.case_id for case in selected_cases],
"bundle": bundle,
"results": {
label: [json.loads(result.to_json_line()) for result in results]
for label, results in results_by_candidate.items()
},
},
ensure_ascii=True,
indent=2,
)
@app.local_entrypoint()
def main(
candidates: str = "nemotron_3_nano_4b_q4_k_m",
candidate_file: str = "",
cases: str = "",
max_cases: int = 1,
max_tokens: int = 0,
context_size: int = 2048,
temperature: float = -1.0,
seed: int = 17,
n_gpu_layers: int = -1,
profile: str = "auto",
) -> None:
candidate_labels = _split_csv(candidates)
case_ids = _split_csv(cases)
candidate_file_json = Path(candidate_file).read_text(encoding="utf-8") if candidate_file else None
if candidate_labels == ["all"]:
from telltale.models.eval_prompts import COMPARISON_MODEL_CANDIDATES
candidate_labels = [candidate.label for candidate in COMPARISON_MODEL_CANDIDATES]
resolved_profile = _resolve_profile(profile, candidate_labels)
if resolved_profile == "nemotron":
max_tokens = max_tokens if max_tokens > 0 else 520
temperature = temperature if temperature >= 0 else 0.55
speech_max_words = 36
rationale_max_words = 44
else:
max_tokens = max_tokens if max_tokens > 0 else 320
temperature = temperature if temperature >= 0 else 0.30
speech_max_words = 16
rationale_max_words = 24
outputs = []
for label in candidate_labels:
try:
output = run_model_eval.remote(
candidate_labels=[label],
candidate_file_json=candidate_file_json,
case_ids=case_ids,
max_cases=max_cases,
max_tokens=max_tokens,
context_size=context_size,
temperature=temperature,
seed=seed,
n_gpu_layers=n_gpu_layers,
speech_max_words=speech_max_words,
rationale_max_words=rationale_max_words,
)
outputs.append({"candidate": label, "ok": True, "output": json.loads(output)})
except Exception as error: # noqa: BLE001 - keep comparing after native/model crashes
outputs.append(
{
"candidate": label,
"ok": False,
"error": f"{type(error).__name__}: {error}",
}
)
print(json.dumps({"isolated_candidate_runs": outputs}, ensure_ascii=True, indent=2))
def _split_csv(value: str) -> list[str]:
return [item.strip() for item in value.split(",") if item.strip()]
def _resolve_profile(profile: str, candidate_labels: list[str]) -> str:
if profile != "auto":
return profile
if candidate_labels and all(label.startswith("nemotron") for label in candidate_labels):
return "nemotron"
return "default"
|