Instructions to use Nabidnur/tracea-explainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Nabidnur/tracea-explainer with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 2,094 Bytes
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license: mit
base_model: Qwen/Qwen3-4B-Instruct
tags:
- tracea
- dynamic-analysis
- reverse-engineering
- agents
- runtime-traces
- lora
- peft
library_name: peft
pipeline_tag: text-generation
---
# tracea-explainer β trace β plain-English behavior reports (planned)
> π§ **Status: adapter training scheduled for Week 4 (target: Nov 10, 2026).** This card will be updated with metrics, samples, and the adapter download when training completes on [TraceBench](https://huggingface.co/datasets/Nabidnur/tracea-bench).
A LoRA adapter for **Qwen3-4B-Instruct** that reads **Evidence Record v1** streams produced by [tracea](https://github.com/Hisernberg/tracea) (hooks, syscall summaries, HTTP/TLS/DNS evidence) and writes a grounded **plain-English behavior report** β every claim tied to an evidence record.
## Why
Agents trace software, but raw evidence is verbose and easy to misread. tracea-explainer is the "narrator": it turns structured runtime evidence into human/agent-readable reports *without* inventing claims β trained to abstain when evidence doesn't support a statement (the "limitations" discipline of Evidence Records).
## Training plan
| Item | Value |
|---|---|
| Base | Qwen/Qwen3-4B-Instruct |
| Data | TraceBench train split (trace JSONL β narrative) |
| Method | LoRA (r=16, Ξ±=32), bf16, seq len 8k |
| Eval | held-out scenarios; factuality vs. ground truth; abstention rate |
| Serving | vLLM/llama.cpp; wired into the [tracea-demo Space](https://huggingface.co/spaces/Nabidnur/tracea-demo) "Auto-explain" tab |
## Intended use
- Narrating **authorized** dynamic-analysis sessions (your apps, devices, captures)
- Security education, debug triage summaries, agent report generation
## Out of scope
Closed-source targets without authorization; malware development; DRM circumvention.
## Links
- π Code: [Hisernberg/tracea](https://github.com/Hisernberg/tracea)
- π Data: [Nabidnur/tracea-bench](https://huggingface.co/datasets/Nabidnur/tracea-bench)
- π Demo: [Nabidnur/tracea-demo](https://huggingface.co/spaces/Nabidnur/tracea-demo)
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