license: apache-2.0
language:
- en
- fi
base_model:
- openai/gpt-oss-20b
pipeline_tag: text-generation
tags:
- instruction-tuning
- distillation
- reasoning
- persona
- agent
- long-context
- research
- experimental
JuuliaDistillEdit (JDE) – GPT-OSS 20B
Version: v0.1-preview Codename: Juulia Special Edition Maintainer: gpt-oss.fi / ghostrouter.fi
✨ Why this model exists
JuuliaDistillEdit (JDE) exists to explore how a strong, open-weight 20B model can be shaped into a personal, consistent, and reasoning-aware assistant using mixed-teacher distillation. Instead of optimizing for raw benchmark scores, JDE focuses on persona stability, instruction clarity, and reasoning flow, serving as a foundation model for the GhostRouter project and future human-AI identity research.
🏗️ Training
Base model: GPT-OSS 20B (open-weight)
Method:
Supervised Fine-Tuning (SFT)
Mixed-teacher distillation
Teacher models (cloud):
GLM-4.6 (reasoning & instruction clarity)
Qwen3-Next-80B (structure & long-context)
GPT-OSS-120B (judging & alignment)
Data sources:
Synthetic instruction datasets
Persona-anchored prompts (“Juulia core”)
Reasoning-style demonstrations
Training goal: Preserve GPT-OSS reasoning strength while adding consistent personality, calm tone, and reduced hallucination drift.
🎯 Intended Use
JDE is designed for:
Personal AI assistants
Research on distillation & persona anchoring
Routing experiments (local ↔ cloud) with GhostRouter
Long-context reasoning assistants
Developer tools and agentic workflows
Not intended for:
Safety-critical or medical decisions
Autonomous control systems
Fully aligned commercial assistants without additional safeguards
⚠️ Limitations
Still inherits biases and errors from teacher models
Reasoning is probabilistic, not guaranteed correct
Not trained on proprietary or private datasets
Alignment is experimental and persona-centric, not policy-centric
This is a research preview, not a final product.
🧪 Evaluation
Qualitative prompt audits
Persona consistency checks
Instruction adherence tests
Used internally as a routing target inside GhostRouter
Formal benchmark scores are intentionally not the focus.
Datasets
This model was trained on synthetic instruction and preference datasets generated via mixed-teacher distillation. No third-party proprietary datasets were used. 📜 License
license: apache-2.0 language: - en - fi base_model: - openai/gpt-oss-20b tags: - instruction-tuning - distillation - personal - research - agent - '- distillation'
Base: Apache 2.0 (GPT-OSS)
Distillation artifacts: Apache 2.0 See LICENSE for full terms.
🔗 Related Projects
GhostRouter – Hybrid AI routing & telemetry https://ghostrouter.fi
GPT-OSS Lab – Open model research https://gpt-oss.fi