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
Download smoke_test.json from Loria-MosAIk/xqdt-e2e-gemma3-4b: direct link, hf CLI and curl.
- Browser
- Download file 5.81 kB
-
https://huggingface.co/Loria-MosAIk/xqdt-e2e-gemma3-4b/resolve/main/smoke_test.json
- Command line
-
hf download hf://Loria-MosAIk/xqdt-e2e-gemma3-4b/smoke_test.json
-
curl -L -o smoke_test.json https://huggingface.co/Loria-MosAIk/xqdt-e2e-gemma3-4b/resolve/main/smoke_test.json
5.81 kB
| { | |
| "base_model": "google/gemma-3-4b-it", | |
| "cases": [ | |
| { | |
| "case": "correct", | |
| "gold": { | |
| "extra": [], | |
| "incorrect": [], | |
| "label": "positive", | |
| "missing": [] | |
| }, | |
| "input_text": "Blue Spice is a coffee shop in city centre.", | |
| "input_triples": [ | |
| "Blue Spice | area | city centre", | |
| "Blue Spice | eat type | coffee shop" | |
| ], | |
| "reference_model_output": "All correct", | |
| "reference_parsed_output": { | |
| "extra": [], | |
| "incorrect": [], | |
| "missing": [] | |
| }, | |
| "sample_id": 0, | |
| "sample_index": 0 | |
| }, | |
| { | |
| "case": "omitted", | |
| "gold": { | |
| "extra": [], | |
| "incorrect": [], | |
| "label": "negative", | |
| "missing": [ | |
| "Mango | buenos aires | Crowne Plaza Hotel", | |
| "The Cricketers | family friendly | yes", | |
| "The Cricketers | eat type | restaurant" | |
| ] | |
| }, | |
| "input_text": "The coffee shop Blue Spice is based near Crowne Plaza Hotel and has a high customer rating of 5 out of 5.", | |
| "input_triples": [ | |
| "Blue Spice | customer rating | 5 out of 5", | |
| "Blue Spice | eat type | coffee shop", | |
| "Blue Spice | near | Crowne Plaza Hotel", | |
| "Mango | buenos aires | Crowne Plaza Hotel", | |
| "The Cricketers | family friendly | yes", | |
| "The Cricketers | eat type | restaurant" | |
| ], | |
| "reference_model_output": "| Type | Triple |\n| ---- | ------ |\n| Missing | [S] Mango [P] buenos aires [O] Crowne Plaza Hotel |\n| Missing | [S] The Cricketers [P] eat type [O] restaurant |\n| Missing | [S] The Cricketers [P] family friendly [O] yes |", | |
| "reference_parsed_output": { | |
| "extra": [], | |
| "incorrect": [], | |
| "missing": [ | |
| "[S] Mango [P] buenos aires [O] Crowne Plaza Hotel", | |
| "[S] The Cricketers [P] eat type [O] restaurant", | |
| "[S] The Cricketers [P] family friendly [O] yes" | |
| ] | |
| }, | |
| "sample_id": 2, | |
| "sample_index": 2 | |
| }, | |
| { | |
| "case": "extra", | |
| "gold": { | |
| "extra": [ | |
| "Blue Spice | eat type | pub" | |
| ], | |
| "incorrect": [], | |
| "label": "negative", | |
| "missing": [] | |
| }, | |
| "input_text": "At the riverside, there is a pub called The Blue Spice.", | |
| "input_triples": [ | |
| "Blue Spice | area | riverside" | |
| ], | |
| "reference_model_output": "| Type | Triple |\n| ---- | ------ |\n| Extra | [S] Blue Spice [P] eat type [O] pub |", | |
| "reference_parsed_output": { | |
| "extra": [ | |
| "[S] Blue Spice [P] eat type [O] pub" | |
| ], | |
| "incorrect": [], | |
| "missing": [] | |
| }, | |
| "sample_id": 6, | |
| "sample_index": 6 | |
| }, | |
| { | |
| "case": "incorrect", | |
| "gold": { | |
| "extra": [], | |
| "incorrect": [ | |
| { | |
| "error_type": "wrong_entity", | |
| "incorrect": "Hot | area | riverside", | |
| "original": "Blue Spice | area | riverside", | |
| "replaced_element": "subject" | |
| }, | |
| { | |
| "error_type": "wrong_entity", | |
| "incorrect": "Blue Spice | family friendly | Politics", | |
| "original": "Blue Spice | family friendly | no", | |
| "replaced_element": "object" | |
| }, | |
| { | |
| "error_type": "wrong_predicate", | |
| "incorrect": "Blue Spice | brand | pub", | |
| "original": "Blue Spice | eat type | pub", | |
| "replaced_element": "predicate" | |
| }, | |
| { | |
| "error_type": "wrong_predicate", | |
| "incorrect": "Blue Spice | address | Rainbow Vegetarian Café", | |
| "original": "Blue Spice | near | Rainbow Vegetarian Café", | |
| "replaced_element": "predicate" | |
| }, | |
| { | |
| "error_type": "wrong_predicate", | |
| "incorrect": "Blue Spice | class | Chinese", | |
| "original": "Blue Spice | food | Chinese", | |
| "replaced_element": "predicate" | |
| } | |
| ], | |
| "label": "negative", | |
| "missing": [] | |
| }, | |
| "input_text": "Blue Spice pub in riverside serves Chinese food. It is not family friendly and can be found near Rainbow Vegetarian Café.", | |
| "input_triples": [ | |
| "Hot | area | riverside", | |
| "Blue Spice | brand | pub", | |
| "Blue Spice | family friendly | Politics", | |
| "Blue Spice | class | Chinese", | |
| "Blue Spice | address | Rainbow Vegetarian Café" | |
| ], | |
| "reference_model_output": "| Type | Triple |\n| ---- | ------ |\n| Incorrect | [S] Hot [P] area [O] riverside |\n| Incorrect | [S] Blue Spice [P] brand [O] pub |\n| Incorrect | [S] Blue Spice [P] family friendly [O] Politics |\n| Incorrect | [S] Blue Spice [P] class [O] Chinese |\n| Incorrect | [S] Blue Spice [P] address [O] Rainbow Vegetarian Café |", | |
| "reference_parsed_output": { | |
| "extra": [], | |
| "incorrect": [ | |
| "[S] Hot [P] area [O] riverside", | |
| "[S] Blue Spice [P] brand [O] pub", | |
| "[S] Blue Spice [P] family friendly [O] Politics", | |
| "[S] Blue Spice [P] class [O] Chinese", | |
| "[S] Blue Spice [P] address [O] Rainbow Vegetarian Café" | |
| ], | |
| "missing": [] | |
| }, | |
| "sample_id": 13, | |
| "sample_index": 13 | |
| } | |
| ], | |
| "comparison": "Compare parsed error units after normalization. Byte-identical generation is not required across inference libraries.", | |
| "reference_backend": "ms-swift PtEngine", | |
| "reference_generation": { | |
| "max_tokens": 1024, | |
| "seed": 2023, | |
| "temperature": 0.3 | |
| }, | |
| "reference_predictions_relative_path": "verifier_train_eval_e2e19/xqdt_outputs/test_predictions_gemma3_4b_6975.json", | |
| "reference_predictions_sha256": "51b9c4e077f7495ca02acbeca9d5798bdbee1ea498c9ec60bf13fd98c08886e0", | |
| "repository": "xqdt-e2e-gemma3-4b", | |
| "schema_version": 1 | |
| } | |