Instructions to use pando-dataset/car-purchase-structured-std with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use pando-dataset/car-purchase-structured-std with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Add car_purchase_d1_it_lora8_20260204_152726_1
Browse files- .gitattributes +1 -0
- car_purchase_d1_it_lora8_20260204_152726_1/adapter_config.json +34 -0
- car_purchase_d1_it_lora8_20260204_152726_1/adapter_model.safetensors +3 -0
- car_purchase_d1_it_lora8_20260204_152726_1/circuit.json +33 -0
- car_purchase_d1_it_lora8_20260204_152726_1/train.json +3 -0
- car_purchase_d1_it_lora8_20260204_152726_1/training_config.json +23 -0
- car_purchase_d1_it_lora8_20260204_152726_1/validation.json +0 -0
.gitattributes
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@@ -37,3 +37,4 @@ tokenizer.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d1_it_lora8_20260204_143127_0/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d1_it_lora8_20260204_143131_1/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d1_it_lora8_20260204_152633_0/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d1_it_lora8_20260204_143127_0/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d1_it_lora8_20260204_143131_1/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d1_it_lora8_20260204_152633_0/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d1_it_lora8_20260204_152726_1/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d1_it_lora8_20260204_152726_1/adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "google/gemma-2-2b-it",
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_bias": false,
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"lora_dropout": 0.0,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 8,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"v_proj",
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"q_proj"
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],
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_rslora": false
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}
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car_purchase_d1_it_lora8_20260204_152726_1/adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:4006fcce6a6d4c089a510e50c01fc7beba2c2a09ed66cca956ec24265f620cab
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size 6403448
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car_purchase_d1_it_lora8_20260204_152726_1/circuit.json
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{
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"expression": "(color == 'Black' and True or not (color == 'Black') and False)",
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"used_fields": [
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"color"
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],
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"num_fields": 1,
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"max_depth": 1,
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"scenario": "car_purchase",
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"circuit_type": "decision_tree",
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"description": "We can decide if a vehicle is good from the following process.\n- if it has color == 'Black' -> yes\n- if it has color != 'Black' -> no\n\nIs this vehicle good?",
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"tree_node": {
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"predicate": "color == 'Black'",
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"field": "color",
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"left": {
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"label": true
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},
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"right": {
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"label": false
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}
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},
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"field_sensitivity": {
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"brand": 0.0,
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"year": 0.0,
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"color": 1.0,
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"horsepower": 0.0,
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"drivetrain": 0.0,
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"mpg": 0.0,
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"seat_capacity": 0.0,
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"interior": 0.0,
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"condition": 0.0,
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"price": 0.0
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}
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}
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car_purchase_d1_it_lora8_20260204_152726_1/train.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:ac486ed67a25722c326f20bbeaaba65a9384ec752a142ac611c7aa8007b5acc6
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size 52790348
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car_purchase_d1_it_lora8_20260204_152726_1/training_config.json
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{
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"base_model": "google/gemma-2-2b-it",
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"original_base_model": "google/gemma-2-2b-it",
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"num_epochs": 1,
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"batch_size": 4,
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"gradient_accumulation_steps": 4,
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"learning_rate": 2e-05,
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"max_seq_length": 512,
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"seed": 2480787754,
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"train_samples": 100000,
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"val_samples": 500,
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"format_style": "structured",
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"shown_fields": null,
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"use_lora": true,
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"use_chat_template": true,
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"alignment_coef": 0.0,
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"alignment_layers": null,
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"mix_fineweb": false,
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"fineweb_ratio": null,
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"lora_rank": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.0
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}
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car_purchase_d1_it_lora8_20260204_152726_1/validation.json
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