Any-to-Any
MLX
Safetensors
gemma4
mlx-vlm
rlcd
multimodal
classification
parallel-inference
image-text-to-text
audio
video
4-bit precision
Instructions to use larkooo/gemma-e2b-rlcd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use larkooo/gemma-e2b-rlcd with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download larkooo/gemma-e2b-rlcd --local-dir gemma-e2b-rlcd
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 1,921 Bytes
53e24ca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | from scripts.benchmark_workloads import agreement, quality, summarize
def test_workload_quality_counts_missing_nested_answers_as_failures():
expected = {"facts": {"cat": True, "dog": False}, "category": ["titanium_a", "titanium_b"]}
actual = {"facts": {"cat": True}, "category": "titanium_b"}
assert quality(actual, expected) == {
"correct": 2,
"scored": 3,
"checks": {"facts.cat": True, "facts.dog": False, "category": True},
}
assert quality(None, expected)["correct"] == 0
assert agreement(actual, None) is None
assert agreement({"a": True}, {"a": True, "b": False}) == {"a": True, "b": False}
def test_benchmark_preserves_failed_runs_and_does_not_award_them_a_speedup():
samples = [
{
"method": "batched",
"seconds": 1,
"valid": True,
"values": {"a": True},
"quality": {"correct": 1, "scored": 1},
},
{
"method": "batched",
"seconds": 3,
"valid": True,
"values": {"a": True},
"quality": {"correct": 1, "scored": 1},
},
{
"method": "normal",
"seconds": 4,
"valid": True,
"values": {"a": True},
"quality": {"correct": 1, "scored": 1},
},
{
"method": "normal",
"seconds": 8,
"valid": False,
"values": None,
"quality": {"correct": 0, "scored": 1},
},
]
summary = summarize(samples, ["batched", "normal"])
assert summary["batched"]["median_seconds"] == 2
assert summary["normal"]["median_seconds"] == 6
assert summary["normal"]["valid_runs"] == 1
assert summary["normal"]["attempted_runs"] == 2
assert summary["normal"]["correct"] == 1
assert summary["normal"]["scored"] == 2
assert summary["normal_over_batched"] is None
|