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] huggingface-cli download --local-dir gemma-e2b-rlcd larkooo/gemma-e2b-rlcd
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 2,678 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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | """Small synthetic text pilot; tests learning plumbing, not general capability."""
import argparse
import itertools
import json
import random
from pathlib import Path
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--output", required=True, type=Path)
args = parser.parse_args()
rng = random.Random(31)
scenes = list(
itertools.product(
["cat", "dog", "bird", "rabbit"],
["red", "blue", "green", "yellow"],
["table", "sofa", "window"],
[1, 2],
)
)
rng.shuffle(scenes)
args.output.mkdir(parents=True, exist_ok=True)
for split, subset in [
("train", scenes[:64]),
("validation", scenes[64:80]),
("test", scenes[80:]),
]:
rows = []
for animal, color, place, count in subset:
animals = ["cat", "dog", "bird", "rabbit"]
colors = ["red", "blue", "green", "yellow"]
rng.shuffle(animals)
rng.shuffle(colors)
rows.append(
{
"id": f"scene-{animal}-{color}-{place}-{count}",
"state": {
"text": f"Near the {place} there {'is one ' + animal if count == 1 else 'are two ' + animal + 's'}. A {color} box is beside them."
},
"questions": {
"animal": {
"type": "choice",
"instructions": "Which kind of animal is present?",
"criteria": {value: "A " + value for value in animals},
},
"color": {
"type": "choice",
"instructions": "What color is the box?",
"criteria": {value: value.capitalize() for value in colors},
},
"count": {
"type": "score",
"instructions": "How many animals are present?",
"criteria": ["One animal", "Two animals"],
},
"cat": {"type": "noul", "instructions": "Is a cat present?"},
},
"targets": {
"animal": animal,
"color": color,
"count": count - 1,
"cat": animal == "cat",
},
}
)
(args.output / f"{split}.jsonl").write_text("".join(json.dumps(row) + "\n" for row in rows))
if __name__ == "__main__":
main()
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