Text Generation
PEFT
Safetensors
English
lora
data-to-text
text-to-data
factual-consistency
hallucination-detection
Instructions to use Loria-MosAIk/xqdt-e2e-gemma3-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Loria-MosAIk/xqdt-e2e-gemma3-4b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-4b-it") model = PeftModel.from_pretrained(base_model, "Loria-MosAIk/xqdt-e2e-gemma3-4b") - Notebooks
- Google Colab
- Kaggle
File size: 1,348 Bytes
f94cee6 | 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 | {
"adapter_model_sha256": "410f60027bb0884923650616b40eeeca780063887717817e8444f91e218775b8",
"base_model": "google/gemma-3-4b-it",
"dataset_release_label": "e2e",
"hyperparameters": {
"bf16": true,
"dataset": [
"triple_error_train"
],
"dataset_num_proc": 1,
"eval_steps": 279,
"fp16": false,
"gradient_accumulation_steps": 4,
"gradient_checkpointing": true,
"learning_rate": 5e-05,
"logging_steps": 93,
"lora_alpha": 32,
"lora_dropout": 0.05,
"lora_rank": 16,
"max_length": 1024,
"max_new_tokens": 64,
"model": "google/gemma-3-4b-it",
"model_type": "gemma3_vision",
"num_train_epochs": 16,
"per_device_eval_batch_size": 4,
"per_device_train_batch_size": 4,
"save_steps": 279,
"save_total_limit": 3,
"seed": 2023,
"system": null,
"target_modules": [
"all-linear"
],
"template": "gemma3_text",
"torch_dtype": "bfloat16",
"train_type": "lora",
"warmup_ratio": 0.0625,
"weight_decay": 0.01
},
"repository": "xqdt-e2e-gemma3-4b",
"schema_version": 1,
"source_checkpoint_relative_path": "checkpoints/checkpoints_e2e19/output_gemma3_4b_e2e19/v0-20260215-222239/checkpoint-6975",
"source_commit": "739c84d868f9d32bcf5c150bdf05c56260cfcaf4",
"training_scope": "joint WebNLG and E2E synthetic data"
}
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