Instructions to use danielfein/raid-ce-gemma4-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danielfein/raid-ce-gemma4-e4b with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("danielfein/raid-ce-gemma4-e4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 849 Bytes
d6fc6f8 | 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 | {
"base_model": "google/gemma-4-E4B-it",
"batch_size": 8,
"beta": 1.0,
"bootstrap_resamples": 1000,
"continuation": {
"learning_rate": 0.0001,
"pairs": 5000,
"schedule_total_steps": 625,
"stop_after_steps": 75
},
"eval": {
"beemo_pairs": 2163,
"raid_standard_test_pairs": 3000
},
"eval_batch_size": 8,
"human_token": "<human>",
"max_length": 512,
"min_learning_rate": 0.00001,
"objective": "ai_pairwise",
"pilot": {
"epochs": 2,
"learning_rate": 0.001,
"pairs": 500,
"updates": 125
},
"prompt_template": "Write {token} text.",
"seeds": [
42,
101,
707
],
"test_source_overlap": 0,
"test_split": "standard_test",
"token": "<ai>",
"train_split": "standard_train_expanded6",
"trainable_parameters": "main AIGEN embedding row only",
"warmup_steps": 20
}
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