Feature Extraction
Transformers
Safetensors
pivot
decision-making
classification
scoring
custom_code
Instructions to use Q1z/Pivot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Q1z/Pivot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Q1z/Pivot", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Q1z/Pivot", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Q1z/Pivot: direct link, hf CLI and curl.
- Browser
- Download file 3.88 kB
-
https://huggingface.co/Q1z/Pivot/resolve/main/config.json
- Command line
-
hf download hf://Q1z/Pivot/config.json
-
curl -L -o config.json https://huggingface.co/Q1z/Pivot/resolve/main/config.json
3.88 kB
| { | |
| "model_type": "pivot", | |
| "architectures": [ | |
| "PivotModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_pivot.PivotConfig", | |
| "AutoModel": "modeling_pivot.PivotModel" | |
| }, | |
| "dsbt_config": { | |
| "backbone": { | |
| "encoder": "huggingface", | |
| "model_id": "LiquidAI/LFM2.5-Encoder-350M", | |
| "revision": "b886781f7c6f10ca9b7096e21b83e30a073c2f39", | |
| "trust_remote_code": true | |
| }, | |
| "scorer": { | |
| "type": "mlp", | |
| "hidden_size": 1024 | |
| }, | |
| "data": { | |
| "k_min": 2, | |
| "k_max": 16, | |
| "max_context_tokens": 512, | |
| "max_option_tokens": 64, | |
| "noul_true": "true", | |
| "noul_false": "false", | |
| "split_ratios": [ | |
| 0.8, | |
| 0.1, | |
| 0.1 | |
| ], | |
| "family_sample_caps": { | |
| "casehold": 0.15 | |
| } | |
| }, | |
| "train": { | |
| "epochs": 8, | |
| "warmup_epochs": 2, | |
| "min_pcgrad_epochs": 4, | |
| "batch_size": 64, | |
| "grad_accum": 1, | |
| "lr_encoder": 1e-05, | |
| "lr_scorer": 5e-05, | |
| "lr_schedule": "cosine", | |
| "lr_warmup_ratio": 0.05, | |
| "lr_min_ratio": 0.1, | |
| "weight_decay": 0.01, | |
| "grad_clip": 1.0, | |
| "precision": "bf16", | |
| "seed": 42, | |
| "decoupling": "pcgrad", | |
| "log_every": 100, | |
| "num_workers": 8, | |
| "early_stop_patience": 2, | |
| "lora": { | |
| "enabled": false | |
| } | |
| }, | |
| "eval": { | |
| "k_strata": [ | |
| 2, | |
| 4, | |
| 8, | |
| 16 | |
| ], | |
| "ece_bins": 15, | |
| "batch_size": 128, | |
| "temperature_scaling": false | |
| }, | |
| "brier": { | |
| "reduction": "mean_over_real_options" | |
| }, | |
| "serving": { | |
| "max_context_tokens": 512, | |
| "max_option_tokens": 128 | |
| } | |
| }, | |
| "encoder_config": { | |
| "transformers_version": "5.17.0", | |
| "architectures": [ | |
| "Lfm2BidirectionalForMaskedLM" | |
| ], | |
| "output_hidden_states": false, | |
| "return_dict": true, | |
| "dtype": "float32", | |
| "chunk_size_feed_forward": 0, | |
| "is_encoder_decoder": false, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "problem_type": null, | |
| "vocab_size": 65536, | |
| "hidden_size": 1024, | |
| "intermediate_size": 6656, | |
| "num_hidden_layers": 16, | |
| "num_attention_heads": 16, | |
| "num_key_value_heads": 8, | |
| "max_position_embeddings": 128000, | |
| "initializer_range": 0.02, | |
| "norm_eps": 1e-05, | |
| "use_cache": false, | |
| "pad_token_id": 0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 7, | |
| "tie_word_embeddings": true, | |
| "rope_parameters": { | |
| "rope_theta": 1000000.0, | |
| "rope_type": "default" | |
| }, | |
| "conv_bias": false, | |
| "conv_L_cache": 3, | |
| "block_multiple_of": 256, | |
| "block_ffn_dim_multiplier": 1.0, | |
| "block_auto_adjust_ff_dim": true, | |
| "full_attn_idxs": null, | |
| "layer_types": [ | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "full_attention", | |
| "conv" | |
| ], | |
| "_name_or_path": "LiquidAI/LFM2.5-Encoder-350M", | |
| "block_dim": 1024, | |
| "block_mlp_init_scale": 1.0, | |
| "block_norm_eps": 1e-05, | |
| "block_out_init_scale": 1.0, | |
| "block_use_swiglu": true, | |
| "block_use_xavier_init": true, | |
| "conv_dim": 1024, | |
| "conv_dim_out": 1024, | |
| "conv_use_xavier_init": true, | |
| "model_type": "lfm2", | |
| "num_heads": 16, | |
| "use_pos_enc": true, | |
| "auto_map": { | |
| "AutoModel": "modeling_lfm2_bidirectional.Lfm2BidirectionalModel", | |
| "AutoModelForMaskedLM": "modeling_lfm2_bidirectional.Lfm2BidirectionalForMaskedLM" | |
| }, | |
| "output_attentions": false | |
| }, | |
| "pivot_training": { | |
| "selected_epoch": 7, | |
| "global_step": 18008, | |
| "method": "CE warmup -> live multiclass Brier + PCGrad", | |
| "probability_contract": "softmax(logits.float())" | |
| } | |
| } | |