wehub-resource-sync
652 lines
20 KiB
Python
652 lines
20 KiB
Python
"""Tests for model metadata normalization in fetcher."""
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import asyncio
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import httpx
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import whichllm.models.fetcher as fetcher
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from whichllm.models.fetcher import (
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_extract_hf_eval_score,
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_extract_published_at,
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_hf_api_url,
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_normalize_param_count,
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_parse_model,
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dicts_to_models,
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models_to_dicts,
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)
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from whichllm.models.types import ModelInfo
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def test_hf_api_url_uses_default_endpoint(monkeypatch):
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monkeypatch.delenv("HF_ENDPOINT", raising=False)
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assert _hf_api_url("models") == "https://huggingface.co/api/models"
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def test_hf_api_url_respects_hf_endpoint(monkeypatch):
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monkeypatch.setenv("HF_ENDPOINT", "https://hf-mirror.example")
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assert _hf_api_url("models/Qwen/Qwen3-8B") == (
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"https://hf-mirror.example/api/models/Qwen/Qwen3-8B"
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)
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def test_hf_api_url_strips_trailing_slash(monkeypatch):
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monkeypatch.setenv("HF_ENDPOINT", "https://hf-mirror.example/")
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assert _hf_api_url("/models") == "https://hf-mirror.example/api/models"
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def test_hf_api_url_rejects_empty_endpoint(monkeypatch):
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monkeypatch.setenv("HF_ENDPOINT", " ")
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try:
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_hf_api_url("models")
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except ValueError as exc:
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assert "HF_ENDPOINT must not be empty" in str(exc)
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else:
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raise AssertionError("expected empty HF_ENDPOINT to raise")
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def test_hf_api_url_rejects_endpoint_without_scheme(monkeypatch):
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monkeypatch.setenv("HF_ENDPOINT", "hf-mirror.example")
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try:
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_hf_api_url("models")
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except ValueError as exc:
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assert "HF_ENDPOINT must start with http:// or https://" in str(exc)
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else:
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raise AssertionError("expected invalid HF_ENDPOINT to raise")
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def test_fetch_models_respects_hf_endpoint(monkeypatch):
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monkeypatch.setenv("HF_ENDPOINT", "https://hf-mirror.example")
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urls: list[str] = []
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encodings: list[str] = []
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async def fake_get_with_retries(client, url: str, **kwargs):
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urls.append(url)
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encodings.append(client.headers["accept-encoding"])
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request = httpx.Request("GET", url)
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if "/models/" in url:
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return httpx.Response(404, request=request)
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return httpx.Response(200, json=[], request=request)
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monkeypatch.setattr(fetcher, "get_with_retries", fake_get_with_retries)
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models = asyncio.run(fetcher.fetch_models(limit=1, include_vision=False))
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assert models == []
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assert urls
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assert all(url.startswith("https://hf-mirror.example/api/") for url in urls)
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assert "https://hf-mirror.example/api/models" in urls
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assert not any(url.startswith("https://huggingface.co/api/") for url in urls)
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assert set(encodings) == {"gzip, deflate"}
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def test_fetch_model_published_at_respects_hf_endpoint(monkeypatch):
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monkeypatch.setenv("HF_ENDPOINT", "https://hf-mirror.example")
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urls: list[str] = []
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encodings: list[str] = []
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async def fake_get(self, url: str, **kwargs):
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urls.append(url)
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encodings.append(self.headers["accept-encoding"])
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return httpx.Response(
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200,
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json={"createdAt": "2026-06-22T00:00:00.000Z"},
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request=httpx.Request("GET", url),
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)
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monkeypatch.setattr(httpx.AsyncClient, "get", fake_get)
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result = asyncio.run(fetcher.fetch_model_published_at(["Qwen/Qwen3-8B"]))
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assert result == {"Qwen/Qwen3-8B": "2026-06-22T00:00:00.000Z"}
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assert urls == ["https://hf-mirror.example/api/models/Qwen/Qwen3-8B"]
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assert encodings == ["gzip, deflate"]
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def test_normalize_param_count_for_quantized_repo_uses_size_hint():
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corrected = _normalize_param_count(
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extracted=5_233_828_308,
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model_id="ISTA-DASLab/gemma-3-27b-it-GPTQ-4b-128g",
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base_model="google/gemma-3-27b-it",
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)
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assert corrected == 27_000_000_000
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def test_normalize_param_count_keeps_reasonable_value():
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kept = _normalize_param_count(
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extracted=11_765_788_416,
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model_id="MaziyarPanahi/gemma-3-12b-it-GGUF",
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base_model="google/gemma-3-12b-it",
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)
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assert kept == 11_765_788_416
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def test_normalize_param_count_with_no_hint_keeps_original():
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kept = _normalize_param_count(
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extracted=3_820_000_000,
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model_id="microsoft/Phi-3-mini-4k-instruct-gguf",
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base_model=None,
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)
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assert kept == 3_820_000_000
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def test_dicts_to_models_normalizes_cached_parameter_count():
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models = dicts_to_models(
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[
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{
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"id": "ISTA-DASLab/gemma-3-27b-it-GPTQ-4b-128g",
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"family_id": "gemma-3-27b",
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"name": "gemma-3-27b-it-GPTQ-4b-128g",
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"parameter_count": 5_233_828_308,
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"downloads": 1,
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"likes": 1,
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"gguf_variants": [],
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"benchmark_scores": {},
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"base_model": "google/gemma-3-27b-it",
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}
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]
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)
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assert len(models) == 1
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assert models[0].parameter_count == 27_000_000_000
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def test_dicts_to_models_refreshes_cached_deepseek_v4_flash_counts():
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models = dicts_to_models(
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[
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{
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"id": "deepseek-ai/DeepSeek-V4-Flash",
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"family_id": "deepseek-v4-flash",
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"name": "DeepSeek-V4-Flash",
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"parameter_count": 158_069_433_298,
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"parameter_count_active": 10_000_000_000,
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"downloads": 1,
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"likes": 1,
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"gguf_variants": [],
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"benchmark_scores": {},
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}
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]
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)
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assert len(models) == 1
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assert models[0].parameter_count == 284_000_000_000
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assert models[0].parameter_count_active == 13_000_000_000
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assert models[0].is_moe is True
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def test_dicts_to_models_uses_case_insensitive_curated_active_params():
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models = dicts_to_models(
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[
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{
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"id": "google/gemma-4-26B-A4B-it",
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"family_id": "gemma-4-26b-a4b",
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"name": "gemma-4-26B-A4B-it",
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"parameter_count": 26_544_131_376,
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"parameter_count_active": None,
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"downloads": 1,
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"likes": 1,
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"gguf_variants": [],
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"benchmark_scores": {},
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}
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]
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)
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assert len(models) == 1
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assert models[0].parameter_count_active == 3_800_000_000
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assert models[0].is_moe is True
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def test_dicts_to_models_recovers_a3b_active_params_from_cached_qwen_model():
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models = dicts_to_models(
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[
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{
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"id": "Qwen/Qwen3.6-35B-A3B",
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"family_id": "qwen3.6-35b-a3b",
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"name": "Qwen3.6-35B-A3B",
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"parameter_count": 35_951_822_704,
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"parameter_count_active": None,
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"architecture": "qwen3_5moe",
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"downloads": 1,
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"likes": 1,
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"gguf_variants": [],
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"benchmark_scores": {},
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}
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]
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)
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assert len(models) == 1
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assert models[0].parameter_count_active == 3_000_000_000
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assert models[0].is_moe is True
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def test_dicts_to_models_recovers_a3b_active_params_from_base_model():
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models = dicts_to_models(
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[
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{
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"id": "local/Qwen36-GGUF",
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"family_id": "qwen36-gguf",
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"name": "Qwen36-GGUF",
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"parameter_count": 34_660_610_688,
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"parameter_count_active": None,
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"architecture": "qwen35moe",
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"downloads": 1,
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"likes": 1,
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"gguf_variants": [],
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"benchmark_scores": {},
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"base_model": "Qwen/Qwen3.6-35B-A3B",
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}
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]
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)
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assert len(models) == 1
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assert models[0].parameter_count_active == 3_000_000_000
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assert models[0].is_moe is True
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def test_dicts_to_models_refreshes_stale_xiaomi_moe_cache_counts():
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models = dicts_to_models(
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[
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{
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"id": "XiaomiMiMo/MiMo-V2.5-Pro",
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"family_id": "mimo-v2.5-pro",
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"name": "MiMo-V2.5-Pro",
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"parameter_count": 58_000_000_000,
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"parameter_count_active": 11_000_000_000,
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"downloads": 1,
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"likes": 1,
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"gguf_variants": [],
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"benchmark_scores": {},
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}
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]
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)
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assert len(models) == 1
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assert models[0].parameter_count == 1_020_000_000_000
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assert models[0].parameter_count_active == 42_000_000_000
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assert models[0].is_moe is True
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def test_dicts_to_models_recovers_missing_known_parameter_count():
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models = dicts_to_models(
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[
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{
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"id": "zai-org/GLM-5",
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"family_id": "glm-5",
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"name": "GLM-5",
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"parameter_count": 0,
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"parameter_count_active": None,
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"downloads": 1,
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"likes": 1,
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"gguf_variants": [],
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"benchmark_scores": {},
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}
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]
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)
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assert len(models) == 1
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assert models[0].parameter_count == 744_000_000_000
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assert models[0].parameter_count_active == 40_000_000_000
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assert models[0].is_moe is True
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def test_parse_model_uses_current_glm5_and_xiaomi_active_counts():
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glm = _parse_model(
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{
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"id": "zai-org/GLM-5",
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"config": {"architectures": ["GlmForCausalLM"]},
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"safetensors": {"total": 753_864_139_008},
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"siblings": [],
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"cardData": {},
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}
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)
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mimo = _parse_model(
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{
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"id": "XiaomiMiMo/MiMo-V2-Flash",
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"config": {"architectures": ["LlamaForCausalLM"]},
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"safetensors": {"total": 309_785_318_400},
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"siblings": [],
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"cardData": {},
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}
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)
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assert glm is not None
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assert glm.parameter_count_active == 40_000_000_000
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assert mimo is not None
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assert mimo.parameter_count_active == 15_000_000_000
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def test_parse_model_recovers_qwen36_a3b_active_params_from_name():
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parsed = _parse_model(
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{
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"id": "Qwen/Qwen3.6-35B-A3B",
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"config": {
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"architectures": ["Qwen3_5MoeForConditionalGeneration"],
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"model_type": "qwen3_5_moe",
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},
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"safetensors": {"total": 35_951_822_704},
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"siblings": [],
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"cardData": {},
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}
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)
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assert parsed is not None
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assert parsed.parameter_count == 35_951_822_704
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assert parsed.parameter_count_active == 3_000_000_000
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assert parsed.is_moe is True
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def test_models_cache_roundtrip_keeps_published_at():
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models = [
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ModelInfo(
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id="Qwen/Qwen3-8B-AWQ",
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family_id="qwen3-8b",
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name="Qwen3-8B-AWQ",
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parameter_count=8_000_000_000,
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published_at="2025-09-17T12:34:56.000Z",
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downloads=123_456,
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likes=789,
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)
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]
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cached = models_to_dicts(models)
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restored = dicts_to_models(cached)
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assert len(restored) == 1
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assert restored[0].published_at == "2025-09-17T12:34:56.000Z"
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assert restored[0].downloads == 123_456
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def test_extract_published_at_prefers_created_at():
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value = _extract_published_at(
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{
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"createdAt": "2025-01-01T00:00:00.000Z",
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"lastModified": "2026-01-01T00:00:00.000Z",
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}
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)
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assert value == "2025-01-01T00:00:00.000Z"
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def test_extract_published_at_falls_back_to_last_modified():
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value = _extract_published_at(
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{
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"lastModified": "2026-01-01T00:00:00.000Z",
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}
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)
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assert value == "2026-01-01T00:00:00.000Z"
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def test_parse_model_keeps_split_gguf_as_single_variant():
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parsed = _parse_model(
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{
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"id": "org/Test-8B-GGUF",
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"config": {
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"architectures": ["LlamaForCausalLM"],
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},
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"safetensors": {"total": 8_000_000_000},
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"siblings": [
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{
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"rfilename": "model-Q4_K_M-00001-of-00002.gguf",
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"size": 2_000_000_000,
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},
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{
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"rfilename": "model-Q4_K_M-00002-of-00002.gguf",
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"size": 2_500_000_000,
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},
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{"rfilename": "model-Q8_0.gguf", "size": 8_000_000_000},
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],
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"cardData": {},
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}
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)
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assert parsed is not None
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q4 = [v for v in parsed.gguf_variants if v.quant_type == "Q4_K_M"]
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q8 = [v for v in parsed.gguf_variants if v.quant_type == "Q8_0"]
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assert len(q4) == 1
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assert len(q8) == 1
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assert q4[0].file_size_bytes == 4_500_000_000
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def test_extract_hf_eval_score_uses_general_datasets_and_median():
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score = _extract_hf_eval_score(
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{
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"evalResults": [
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{
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"filename": ".eval_results/mmlu-pro.yaml",
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"data": {"dataset": {"id": "TIGER-Lab/MMLU-Pro"}, "value": 48.3},
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},
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{
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"filename": ".eval_results/gsm8k.yaml",
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"data": {"dataset": {"id": "openai/gsm8k"}, "value": 84.5},
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},
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{
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"filename": ".eval_results/hle_medium_with_tools.yaml",
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"data": {
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"dataset": {"id": "cais/hle"},
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"value": 99.0,
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"notes": "Reasoning: medium, With tools",
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},
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},
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{
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"filename": ".eval_results/swe_bench.yaml",
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"data": {
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"dataset": {"id": "SWE-bench/SWE-bench_Verified"},
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"value": 53.2,
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},
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},
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]
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}
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)
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# general対象(mmlu/gsm8k)のみ集計し、中央値を使う。
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assert score == 66.4
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def test_parse_model_extracts_hf_eval_benchmark_score():
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parsed = _parse_model(
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{
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"id": "meta-llama/Llama-3.1-8B-Instruct",
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"config": {"architectures": ["LlamaForCausalLM"]},
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"safetensors": {"total": 8_000_000_000},
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"siblings": [],
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"cardData": {},
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"evalResults": [
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{
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"filename": ".eval_results/mmlu-pro.yaml",
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"data": {"dataset": {"id": "TIGER-Lab/MMLU-Pro"}, "value": 48.3},
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},
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{
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"filename": ".eval_results/gsm8k.yaml",
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"data": {"dataset": {"id": "openai/gsm8k"}, "value": 84.5},
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},
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],
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}
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)
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assert parsed is not None
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assert parsed.benchmark_scores.get("hf_eval") == 66.4
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def test_deepseek_v4_flash_uses_model_card_counts_over_hf_tensor_metadata():
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"""DeepSeek V4 Flash's mixed-precision HF tensor metadata reports a
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smaller stored tensor count than the model-card total. Ranking and GGUF
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synthesis must use the published model capacity instead."""
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parsed = _parse_model(
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{
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"id": "deepseek-ai/DeepSeek-V4-Flash",
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"config": {
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"architectures": ["DeepseekV4ForCausalLM"],
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"model_type": "deepseek_v4",
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"quantization_config": {"quant_method": "fp8"},
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},
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"safetensors": {
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"total": 158_069_433_298,
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"parameters": {
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"BF16": 1_415_259_264,
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"F8_E8M0": 8_858_737_664,
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"F8_E4M3": 6_023_020_544,
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"I8": 141_733_920_768,
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},
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},
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"siblings": [],
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"cardData": {},
|
|
}
|
|
)
|
|
|
|
assert parsed is not None
|
|
assert parsed.parameter_count == 284_000_000_000
|
|
assert parsed.parameter_count_active == 13_000_000_000
|
|
|
|
|
|
def test_parse_model_resolves_gemma3_sliding_window():
|
|
parsed = _parse_model(
|
|
{
|
|
"id": "google/gemma-3-27b-it",
|
|
"config": {
|
|
"architectures": ["Gemma3ForConditionalGeneration"],
|
|
"model_type": "gemma3",
|
|
"sliding_window": 1024,
|
|
"sliding_window_pattern": 6,
|
|
"max_position_embeddings": 131072,
|
|
},
|
|
"safetensors": {"total": 27_000_000_000},
|
|
"siblings": [],
|
|
"cardData": {},
|
|
}
|
|
)
|
|
assert parsed is not None
|
|
assert parsed.sliding_window == 1024
|
|
assert parsed.sliding_window_global_ratio == 1.0 / 6.0
|
|
|
|
|
|
def test_parse_model_resolves_gpt_oss_sliding_window():
|
|
parsed = _parse_model(
|
|
{
|
|
"id": "openai/gpt-oss-20b",
|
|
"config": {
|
|
"architectures": ["GptOssForCausalLM"],
|
|
"model_type": "gpt_oss",
|
|
"sliding_window": 128,
|
|
"max_position_embeddings": 131072,
|
|
},
|
|
"safetensors": {"total": 20_000_000_000},
|
|
"siblings": [],
|
|
"cardData": {},
|
|
}
|
|
)
|
|
assert parsed is not None
|
|
assert parsed.sliding_window == 128
|
|
assert parsed.sliding_window_global_ratio == 0.5
|
|
|
|
|
|
def test_parse_model_does_not_honor_mistral_sliding_window():
|
|
"""Mistral declares sliding_window in config but runtimes ignore it."""
|
|
parsed = _parse_model(
|
|
{
|
|
"id": "mistralai/Mistral-7B-v0.1",
|
|
"config": {
|
|
"architectures": ["MistralForCausalLM"],
|
|
"model_type": "mistral",
|
|
"sliding_window": 4096,
|
|
"max_position_embeddings": 32768,
|
|
},
|
|
"safetensors": {"total": 7_000_000_000},
|
|
"siblings": [],
|
|
"cardData": {},
|
|
}
|
|
)
|
|
assert parsed is not None
|
|
assert parsed.sliding_window is None
|
|
assert parsed.sliding_window_global_ratio is None
|
|
|
|
|
|
def test_parse_model_respects_disabled_sliding_window():
|
|
parsed = _parse_model(
|
|
{
|
|
"id": "google/gemma-3-4b-it",
|
|
"config": {
|
|
"architectures": ["Gemma3ForConditionalGeneration"],
|
|
"model_type": "gemma3",
|
|
"sliding_window": 1024,
|
|
"use_sliding_window": False,
|
|
},
|
|
"safetensors": {"total": 4_000_000_000},
|
|
"siblings": [],
|
|
"cardData": {},
|
|
}
|
|
)
|
|
assert parsed is not None
|
|
assert parsed.sliding_window is None
|
|
assert parsed.sliding_window_global_ratio is None
|
|
|
|
|
|
def test_parse_model_does_not_honor_gemma1_window():
|
|
"""Gemma-1 has no sliding window; the gemma2/gemma3 keys must not match it."""
|
|
parsed = _parse_model(
|
|
{
|
|
"id": "google/gemma-7b",
|
|
"config": {
|
|
"architectures": ["GemmaForCausalLM"],
|
|
"model_type": "gemma",
|
|
},
|
|
"safetensors": {"total": 7_000_000_000},
|
|
"siblings": [],
|
|
"cardData": {},
|
|
}
|
|
)
|
|
assert parsed is not None
|
|
assert parsed.sliding_window is None
|
|
|
|
|
|
def test_parse_model_sliding_window_from_gguf_metadata():
|
|
"""GGUF-only repos: trust the authoritative GGUF architecture metadata."""
|
|
parsed = _parse_model(
|
|
{
|
|
"id": "unsloth/gemma-3-12b-it-GGUF",
|
|
"config": {},
|
|
"gguf": {"architecture": "gemma3"},
|
|
"safetensors": {"total": 12_000_000_000},
|
|
"siblings": [],
|
|
"cardData": {},
|
|
}
|
|
)
|
|
assert parsed is not None
|
|
assert parsed.sliding_window == 1024
|
|
assert parsed.sliding_window_global_ratio == 1.0 / 6.0
|
|
|
|
|
|
def test_parse_model_sliding_window_unset_without_metadata():
|
|
"""With neither HF config nor GGUF arch metadata, SWA stays unhonored.
|
|
|
|
Detection is limited to authoritative metadata; a GGUF-only repo whose id
|
|
merely names an SWA family keeps the conservative full-context estimate
|
|
rather than being guessed at from the id.
|
|
"""
|
|
parsed = _parse_model(
|
|
{
|
|
"id": "TheBloke/gemma-2-9b-it-GGUF",
|
|
"config": {},
|
|
"gguf": {},
|
|
"safetensors": {"total": 9_000_000_000},
|
|
"siblings": [],
|
|
"cardData": {},
|
|
}
|
|
)
|
|
assert parsed is not None
|
|
assert parsed.sliding_window is None
|
|
assert parsed.sliding_window_global_ratio is None
|
|
|
|
|
|
def test_models_cache_roundtrip_keeps_sliding_window():
|
|
models = [
|
|
ModelInfo(
|
|
id="google/gemma-3-27b-it",
|
|
family_id="gemma-3-27b",
|
|
name="gemma-3-27b-it",
|
|
parameter_count=27_000_000_000,
|
|
sliding_window=1024,
|
|
sliding_window_global_ratio=1.0 / 6.0,
|
|
)
|
|
]
|
|
restored = dicts_to_models(models_to_dicts(models))
|
|
assert restored[0].sliding_window == 1024
|
|
assert restored[0].sliding_window_global_ratio == 1.0 / 6.0
|