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arriella
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Instructions to use UnaverageTech411/arriella-docs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UnaverageTech411/arriella-docs with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UnaverageTech411/arriella-docs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Arriella Flagship
General-purpose core text tier · Heretic + premium distillation
| Field | Value |
|---|---|
| Fleet ID | arriella-flagship |
| Ollama | ollama run arriella-flagship |
| Parameters | ~1.5B (Ollama) |
| Foundation (clay only) | Qwen/Qwen2.5-1.5B-Instruct |
| Merged weights | fleet/flagship-qwen15/model |
| Demo priority | Primary |
| Business role | General ops, bakeoffs, primary local assistant |
Description
Arriella Flagship is a custom-trained fleet product — not stock Qwen. Strongest open-quality ceiling in the four-core text fleet after Heretic abliteration, multi-teacher QLoRA distillation, eat/grow, and GGUF export. Core siblings: Scout, Growth, Ascension. Grapevine is a separate multimodal extension.
Features
- Heretic + premium teacher distill (Hermes / Orca / Smoltalk-class mixes)
- Size-tier winner vs stock
llama3.2:1bon the combined API gauntlet (see docs) - Thinking format + math module; Gemma3-routed vision (text descriptions)
- First-class MIP load target for attention visualization
Intended uses
- Local demos and bakeoffs
- Deepest local Arriella answers on ≤8 GB for general business tasks
- MIP interior inspection
Out of scope
- Claiming to be stock Qwen
- Native pixel VLM weights in this checkpoint
- Claude / frontier closed-model parity
- Claiming Ascension is weaker on every reasoning task without a bakeoff
Benchmarks
See docs/benchmarks/README.md.
Hub card stub
docs/papers/hf-cards/flagship.md
Use
ollama run arriella-flagship