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119228a6db
Add official Gemma 4 12B-it plus QAT-INT4/INT8 catalog entries (with their GGUF sources), QAT quantization support across the quant tables and the prequantized-prefix list, and the missing RTX 3050 / 3050 Ti memory bandwidth so speed estimates stop falling back to the generic cuda value.
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
from services.hwfit.fit import rank_models
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from services.hwfit.models import get_models, is_prequantized
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def _8gb_vram_system():
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return {
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"has_gpu": True,
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"backend": "cuda",
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"gpu_name": "NVIDIA GeForce RTX 4060",
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"gpu_vram_gb": 8.0,
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"gpu_count": 1,
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"available_ram_gb": 32.0,
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"total_ram_gb": 32.0,
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}
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def test_gemma4_12b_in_catalog():
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catalog = {m["name"]: m for m in get_models()}
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assert "google/gemma-4-12B-it" in catalog, "gemma-4-12B-it missing from catalog"
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def test_gemma4_12b_has_gguf_source():
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catalog = {m["name"]: m for m in get_models()}
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entry = catalog["google/gemma-4-12B-it"]
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assert entry.get("gguf_sources"), "gemma-4-12B-it has no gguf_sources"
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repos = [s["repo"] for s in entry["gguf_sources"]]
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assert "unsloth/gemma-4-12B-it-GGUF" in repos
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def test_gemma4_12b_rank_models_returns_it_for_8gb_vram():
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results = rank_models(_8gb_vram_system(), search="gemma-4-12B-it", limit=20)
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names = [r["name"] for r in results]
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assert "google/gemma-4-12B-it" in names, "rank_models did not return gemma-4-12B-it for 8 GB VRAM"
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def test_gemma4_12b_qat_entries_in_catalog():
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catalog = {m["name"]: m for m in get_models()}
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assert "google/gemma-4-12B-it-qat-int4" in catalog
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assert "google/gemma-4-12B-it-qat-int8" in catalog
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def test_gemma4_12b_qat_entries_are_prequantized():
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catalog = {m["name"]: m for m in get_models()}
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assert is_prequantized(catalog["google/gemma-4-12B-it-qat-int4"])
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assert is_prequantized(catalog["google/gemma-4-12B-it-qat-int8"])
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def test_gemma4_12b_qat_entries_have_no_gguf():
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catalog = {m["name"]: m for m in get_models()}
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assert catalog["google/gemma-4-12B-it-qat-int4"]["gguf_sources"] == []
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assert catalog["google/gemma-4-12B-it-qat-int8"]["gguf_sources"] == []
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