Instructions to use pando-dataset/car-purchase-natural-std with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use pando-dataset/car-purchase-natural-std with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Add car_purchase_d3_it_lora8_20260227_235758_4
Browse files- .gitattributes +1 -0
- car_purchase_d3_it_lora8_20260227_235758_4/adapter_config.json +34 -0
- car_purchase_d3_it_lora8_20260227_235758_4/adapter_model.safetensors +3 -0
- car_purchase_d3_it_lora8_20260227_235758_4/circuit.json +77 -0
- car_purchase_d3_it_lora8_20260227_235758_4/train.json +3 -0
- car_purchase_d3_it_lora8_20260227_235758_4/training_config.json +24 -0
- car_purchase_d3_it_lora8_20260227_235758_4/validation.json +0 -0
.gitattributes
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@@ -77,3 +77,4 @@ car_purchase_d3_it_lora8_20260227_221239_4/train.json filter=lfs diff=lfs merge=
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car_purchase_d3_it_lora8_20260227_221812_5/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d3_it_lora8_20260227_230532_4/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d3_it_lora8_20260227_231057_5/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d3_it_lora8_20260227_221812_5/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d3_it_lora8_20260227_230532_4/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d3_it_lora8_20260227_231057_5/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d3_it_lora8_20260227_235758_4/train.json filter=lfs diff=lfs merge=lfs -text
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car_purchase_d3_it_lora8_20260227_235758_4/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_d3_it_lora8_20260227_235758_4/adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9d31e4d3081c969656da4ea077e96ed1e1360fa3dab66a1c3e5b49394751fb76
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size 6403448
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car_purchase_d3_it_lora8_20260227_235758_4/circuit.json
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{
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"expression": "(brand == 'Toyota' and (color == 'White' and (seat_capacity <= 4 and False or not (seat_capacity <= 4) and True) or not (color == 'White') and (seat_capacity <= 6 and False or not (seat_capacity <= 6) and True)) or not (brand == 'Toyota') and (seat_capacity <= 4 and (color == 'White' and False or not (color == 'White') and True) or not (seat_capacity <= 4) and (color == 'Black' and False or not (color == 'Black') and True)))",
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"used_fields": [
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"seat_capacity",
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"brand",
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"color"
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],
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"num_fields": 3,
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"max_depth": 3,
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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 brand == 'Toyota'\n-- if it has color == 'White'\n--- if it has seat capacity <= 4 -> no\n--- if it has seat capacity > 4 -> yes\n-- if it has color != 'White'\n--- if it has seat capacity <= 6 -> no\n--- if it has seat capacity > 6 -> yes\n- if it has brand != 'Toyota'\n-- if it has seat capacity <= 4\n--- if it has color == 'White' -> no\n--- if it has color != 'White' -> yes\n-- if it has seat capacity > 4\n--- if it has color == 'Black' -> no\n--- if it has color != 'Black' -> yes\n\nIs this vehicle good?",
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"tree_node": {
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"predicate": "brand == 'Toyota'",
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"field": "brand",
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"left": {
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"predicate": "color == 'White'",
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"field": "color",
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"left": {
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"predicate": "seat_capacity <= 4",
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"field": "seat_capacity",
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"left": {
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"label": false
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},
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"right": {
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"label": true
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}
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},
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"right": {
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"predicate": "seat_capacity <= 6",
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"field": "seat_capacity",
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"left": {
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"label": false
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},
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"right": {
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"label": true
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}
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}
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},
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"right": {
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"predicate": "seat_capacity <= 4",
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"field": "seat_capacity",
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"left": {
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"predicate": "color == 'White'",
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"field": "color",
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"left": {
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"label": false
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},
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"right": {
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"label": true
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}
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},
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"right": {
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"predicate": "color == 'Black'",
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"field": "color",
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"left": {
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"label": false
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},
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"right": {
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"label": true
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}
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}
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}
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},
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"field_sensitivity": {
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"brand": 0.3769,
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"year": 0.0,
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"color": 0.6271,
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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": 1.0,
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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_d3_it_lora8_20260227_235758_4/train.json
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:665191360d54843f9c10bc78d8998f0577929a155979966a3d488b781082f542
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size 50090780
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car_purchase_d3_it_lora8_20260227_235758_4/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": 1394131008,
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"train_samples": 100000,
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"val_samples": 500,
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"format_style": "natural",
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"training_format": null,
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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_d3_it_lora8_20260227_235758_4/validation.json
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