Text Generation
PEFT
Safetensors
English
lora
data-to-text
text-to-data
factual-consistency
hallucination-detection
Instructions to use Loria-MosAIk/xqdt-e2e-qwen3-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Loria-MosAIk/xqdt-e2e-qwen3-1.7b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "Loria-MosAIk/xqdt-e2e-qwen3-1.7b") - Notebooks
- Google Colab
- Kaggle
File size: 942 Bytes
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"original_base_model_reference_was_machine_local": true,
"published_base_model_name_or_path": "Qwen/Qwen3-1.7B",
"schema_version": 1,
"source_checkpoint_relative_path": "checkpoints/checkpoints_e2e19/output_qwen3_1.7e2e/v1-20260214-160705/checkpoint-15345",
"source_commit": "739c84d868f9d32bcf5c150bdf05c56260cfcaf4",
"source_files": {
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"args.json": "2646c897ae9f05482826ec4fc9e609ab20b6aa8a34f83e469585071d69484c56",
"reference_predictions": "7504136d3afa18b015b837316eecfeaddbda62d3f22789094005bd5ec1240010"
}
}
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