Transformers
English
arriella
infinidev
documentation
technical-report
model-card
local-llm
not-for-inference
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
Grapevine — multimodal fleet extension
Grapevine is not a fifth core text model. The core fleet remains Scout · Growth · Flagship · Ascension (all text-only). Grapevine is the local Omni multimodal extension.
Long-form benchmark narrative (root): ../../grapevine.md. HF paper draft: ../papers/grapevine-hf.md.
Identity
| Field | Value |
|---|---|
| Ollama | arriella-grapevine |
| Company | Infinidev Corp |
| Leads | Beelzebub4888, Tcoder |
| Foundation | Qwen/Qwen2.5-Omni-3B |
| Capability target | thinkingmachines/Inkling (target only — no Inkling weights) |
| Work dir | fleet/inkling/ |
What it is
- Q8_0 Omni base + Arriella runtime LoRA + multimodal projector
- Ollama capabilities advertised today: completion, vision, audio (not video, not speech-out)
- Pre-export Transformers path also validated video-frame understanding
6.3 GB installed; much slower than Flagship (6× on local smoke)
Training / repair path
# Local Omni adapter (see scripts for full flags)
.\.venv\Scripts\python.exe scripts\train_arriella_inkling_local.py --help
# Release / deployment repair datasets → data/grapevine_*
.\.venv\Scripts\python.exe scripts\prepare_grapevine_release_repair.py --help
.\.venv\Scripts\python.exe scripts\prepare_grapevine_deployment_repair.py --help
# Merge / verify / Ollama smoke
.\.venv\Scripts\python.exe scripts\merge_grapevine_release.py --help
.\.venv\Scripts\python.exe scripts\verify_grapevine_release.py --help
.\.venv\Scripts\python.exe scripts\test_grapevine_ollama.py --help
# vs Flagship smoke
.\.venv\Scripts\python.exe scripts\benchmark_grapevine_vs_flagship.py
Build notes that still say “Inkling” as product name: fleet/inkling/INKLING_BUILD_SPEC.md (historical; product is Grapevine).
Business / field use
Prefer Grapevine when the workflow needs real image or audio input (inspections, screenshots, voice notes → structured text). Prefer Flagship when you need fast text-only ops or Ollama tools. Do not treat Grapevine as a blanket quality upgrade over Flagship.
Honest limitations (Jul 2026)
- Treat current Ollama build as a multimodal technical preview
- Identity/provenance under Ollama runtime-adapter can still drift vs Transformers acceptance
- Never claim modalities that the installed Ollama manifest does not advertise
- Prove modalities with payloads, not self-description
Chat
ollama run arriella-grapevine