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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.
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