Add pinned source architecture config and provenance

CONFIG_PROVENANCE.md

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+# Root config provenance
+
+The root `config.json` is an unmodified copy of the full upstream model config from [`fastino/gliner2.5-small-v1`](https://huggingface.co/fastino/gliner2.5-small-v1/tree/7e6f537f10337497069276892a5ef435028252ce) at `7e6f537f10337497069276892a5ef435028252ce`, path `config.json`.
+
+SHA-256: `0b7d9e1401ceeb83e992ec66d2f93bff7e5646428f1b4706ec527cf88f53578a`.
+
+This file describes the source architecture. It does not make the Core ML package a standalone Transformers AutoModel checkpoint. Use the repository's Core ML runtime and its included tokenizer, decision heads, and calibration where applicable.

config.json

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+{
+  "architecture": "boundary",
+  "architecture_version": 1,
+  "architectures": [
+    "BoundaryExtractor"
+  ],
+  "attn_implementation": "sdpa",
+  "boundary_head": {
+    "abstention_loss_weight": 0.2,
+    "abstention_threshold": 0.5,
+    "adaptive_threshold": false,
+    "bidirectional_proposals": true,
+    "boundary_attention_heads": 4,
+    "boundary_attention_layers": 2,
+    "boundary_attention_window": 128,
+    "boundary_dim": 128,
+    "boundary_ffn_multiplier": 2.0,
+    "boundary_focal_clip": 0.05,
+    "boundary_focal_gamma_negative": 2.0,
+    "boundary_focal_gamma_positive": 0.0,
+    "boundary_marginal_loss": "asymmetric_focal",
+    "boundary_negative_weight": 0.5,
+    "boundary_refinement_layers": 1,
+    "boundary_top_k_alpha": 0.08,
+    "boundary_top_k_bucket": 8,
+    "boundary_top_k_max": 128,
+    "candidate_attention_heads": 4,
+    "candidate_attention_layers": 0,
+    "candidate_budget": 192,
+    "candidate_pool": "shared",
+    "classification_loss_weight": 1.0,
+    "classification_temperature": 1.0,
+    "consistency_loss_weight": 0.1,
+    "consistency_warmup_steps": 2000,
+    "content_dim": 64,
+    "content_soft_max_pool": false,
+    "count_loss_weight": 0.2,
+    "directional_relation_states": true,
+    "dropout": 0.1,
+    "enable_abstention": true,
+    "enable_count_head": true,
+    "enable_records": true,
+    "enable_relations": true,
+    "enable_rotary_endpoints": true,
+    "enable_span_content": true,
+    "end_block_size": 256,
+    "end_top_k": 24,
+    "endpoint_difference_features": true,
+    "ends_per_start": 12,
+    "export_mode": "auto",
+    "hard_negative_keep_all_when_absent": true,
+    "hard_negatives_per_positive": 20,
+    "loss_reduction": "sum",
+    "max_gold_per_query": 64,
+    "max_negative_queries_per_batch": 64,
+    "min_pool_per_query": 8,
+    "minimum_hard_negatives": 16,
+    "multihead_pair_compat_heads": 8,
+    "negative_query_ratio": 1.0,
+    "overlap_policy": "flat",
+    "pair_dim": 128,
+    "pair_temperature": 1.0,
+    "pool_boundary_top_k": 32,
+    "pool_size": 192,
+    "proposal_loss_weight": 0.3,
+    "query_attention_layers": 0,
+    "query_conditioned_inside_weight": true,
+    "record_anchor_proposal_threshold": 0.2,
+    "record_anchor_threshold": 0.5,
+    "record_dim": 128,
+    "record_field_threshold": 0.5,
+    "record_instance_queries": 32,
+    "record_loss_weight": 1.0,
+    "record_temperature": 1.0,
+    "relation_argument_proposal_threshold": 0.2,
+    "relation_biaffine_content": true,
+    "relation_heads_per_type": 32,
+    "relation_loss_weight": 1.0,
+    "relation_pair_cap": 64,
+    "relation_tails_per_type": 32,
+    "relation_temperature": 1.0,
+    "rerank_listwise_weight": 0.3,
+    "reranker_endpoint_compat": true,
+    "rotary_base": 10000.0,
+    "soft_iou_anneal_steps": 20000,
+    "soft_iou_aux_weight": 0.2,
+    "start_top_k": 24,
+    "starts_per_end": 12,
+    "training_candidate_budget": 192,
+    "use_inside_evidence": true,
+    "vectorized_pair_elements": 16777216
+  },
+  "config_version": 3,
+  "max_len": 4096,
+  "model_name": "microsoft/deberta-v3-xsmall",
+  "model_type": "extractor",
+  "token_pooling": "first",
+  "transformers_version": "5.8.0"
+}