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LICENSE

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NOTICE

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+EviWeave-VisualPlanner-8B
+
+This checkpoint is a modified derivative of the locally archived Qwen3-8B base
+model. The modification consists of EviWeave visual-planning fine-tuning and
+the corresponding submission-stage LoRA adapter.
+
+The Apache License 2.0 text shipped with the base model is included as LICENSE.
+Source datasets and generated training assets retain their own attribution,
+redistribution, and commercial-use conditions. See the project dataset card,
+training asset registry, and MODEL_CARD.md for the research-release boundary.

PACKAGE_INVENTORY.json

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+    {
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+  ]
+}

README.md

@@ -1,3 +1,57 @@
 ---
-license: mit
+base_model: Qwen/Qwen3-8B
+library_name: peft
+pipeline_tag: text-generation
+license: apache-2.0
+tags:
+- lora
+- visual-planning
+- provenance
+- report-generation
+- peer-review-artifact
 ---
+
+# EviWeave-VisualPlanner-8B
+
+This submission-stage model package contains the complete PEFT/LoRA adapter
+used by EviWeave's structured visual planner. It does **not** contain the
+Qwen3-8B base weights or the 16 GB merged checkpoint. Obtain the base model
+separately from `Qwen/Qwen3-8B` under its original terms.
+
+## Included
+
+- `adapter_model.safetensors`: complete learned LoRA parameters (rank 32).
+- `adapter_config.json`: portable PEFT configuration with the public base-model ID.
+- `metadata/training_audit.json`: canonical-target and repair-row audit.
+- `metadata/leakage_report.json`: source/task/claim isolation audit.
+- `release_manifest.json`: checksum and explicit release boundary.
+
+## Load with Transformers and PEFT
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_id = "Qwen/Qwen3-8B"
+tokenizer = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
+base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")
+model = PeftModel.from_pretrained(base, "/path/to/EviWeave-VisualPlanner-8B")
+model.eval()
+```
+
+The canonical VisualPlan prompt/compiler and verifier are in the separate code
+package. This adapter is intended for structured visual planning, not as a
+stand-alone factual extractor or general report writer. Generated plans must
+still pass deterministic schema, grounding, fidelity, and rendering checks.
+
+## Training summary
+
+The planner used 6,904 first-stage SFT examples over 2,040 source-disjoint tasks
+(5,399 final-plan and 1,505 verifier-passing repair rows), followed by a
+1,920-example Boundary SFT stage (480 final-plan and 1,440 repair rows).
+
+## Limitations
+
+The package omits training data, trainer state, merged weights, and external API
+models. It therefore supports inference and model inspection but not complete
+training reproduction during peer review.

SHA256SUMS

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adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3-8B",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [],
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "o_proj",
+    "down_proj",
+    "up_proj",
+    "gate_proj",
+    "k_proj",
+    "q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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metadata/leakage_report.json

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+{
+  "candidate": "runs/visualplan_boundary_v2/train.tasks.jsonl",
+  "protected": [
+    "runs/visualplan_boundary_v2/dev.tasks.jsonl",
+    "runs/eval_protocol_v2/tasks.test.jsonl",
+    "runs/eval_protocol_v3_ood/tasks.clean.jsonl",
+    "runs/eval_protocol_v3_ood/tasks.distractor.jsonl",
+    "runs/eval_protocol_v3_ood/tasks.repair.jsonl",
+    "runs/eval_protocol_blind_v1/tasks.clean.jsonl"
+  ],
+  "candidate_rows": 480,
+  "protected_rows": 757,
+  "base_uid_overlap_count": 0,
+  "claim_id_overlap_count": 0,
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+  "fail_on_source_overlap": true,
+  "passed": true,
+  "examples": {
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+    "claim_ids": [],
+    "sources": []
+  }
+}

metadata/training_audit.json

@@ -0,0 +1,39 @@
+{
+  "sft": {
+    "path": "runs/visualplan_boundary_v2/train.sft.jsonl",
+    "rows": 1920,
+    "unique_tasks": 480,
+    "trace_types": {
+      "boundary_v2_final_only_sft": 480,
+      "verifier_guided_repair_sft": 1440
+    },
+    "repair_rows": 1440,
+    "invalid_json_targets": 0,
+    "noncanonical_targets": 0,
+    "column_shapes": {
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+    }
+  },
+  "preferences": {
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+    "rows": 1440,
+    "unique_tasks": 480,
+    "failure_reasons": {
+      "wrong_diagram_type": 480,
+      "missing_semantic_node": 480,
+      "reversed_semantic_edge": 480
+    },
+    "column_shapes": {
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+    "invalid_chosen_json": 0,
+    "invalid_rejected_json": 0,
+    "noncanonical_chosen": 0,
+    "identical_pairs": 0,
+    "nonpositive_score_gaps": 0,
+    "score_gap_mean": 0.5,
+    "score_gap_min": 0.5
+  },
+  "passed": true,
+  "failures": []
+}

release_manifest.json

@@ -0,0 +1,20 @@
+{
+  "name": "EviWeave-VisualPlanner-8B",
+  "release_scope": "complete adapter; base and merged weights excluded",
+  "base_model": "Qwen/Qwen3-8B",
+  "peft_type": "LoRA",
+  "rank": 32,
+  "lora_alpha": 64,
+  "lora_dropout": 0.05,
+  "adapter_bytes": 349243752,
+  "adapter_sha256": "e2b25e761d2cf25fb9cbc5a3b63f7098ae33fef17587574350127685e096f7fb",
+  "original_frozen_adapter_sha256": "e2b25e761d2cf25fb9cbc5a3b63f7098ae33fef17587574350127685e096f7fb",
+  "excluded": [
+    "Qwen3-8B base weights",
+    "16 GB merged checkpoint",
+    "optimizer/trainer state",
+    "training corpus",
+    "deployment logs",
+    "provider credentials"
+  ]
+}