| --- |
| 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. |
|
|
