TenaOS

TenaOS - Gemma 4 E4B Task-Tagged LoRA

TenaOS is a local-first clinical AI operating system for primary-care workflows. This repository hosts the Gemma 4 E4B runtime artifacts used by TenaOS, including the base BF16 GGUF model, multimodal projector, and the task-tagged LoRA adapter trained for TenaOS clinical-informatics workflows.

TenaOS follows a constrained clinical-agent pattern: Gemma proposes, local knowledge bases ground, deterministic middleware validates, and clinicians review before anything is persisted to OpenMRS.

Release Status

This model card describes the current released adapter and merged GGUF artifacts. The documentation, metadata, and model-card charts have been refreshed to match the released weights.

The adapter was trained from real multi-turn production workflow traces with assistant-turn loss masking. Workflow-level validation is still required before making task-by-task performance claims against the base model.

Files

The base BF16 GGUF and projector filenames are preserved for compatibility with the TenaOS bootstrap scripts.

Task Tags

The adapter is trained as a single multi-task adapter routed by explicit task tags:

Training Summary

The adapter was trained on curated, task-tagged clinical-informatics workflow traces reconstructed from the TenaOS production stack.

Dataset And SFT Format

The corpus keeps seven workflow families and uses real multi-turn ShareGPT-style conversations reconstructed from production event traces, including system, user, assistant tool-call, and tool-result turns where available. The training script applies the Gemma 4 chat template and masks loss to assistant turns only.

The released training corpus is available under training_corpus/. It is synthetic, teacher-generated, task-tagged training data, not real patient records and not clinical validation data. The corresponding training and merge scripts are available under training_code/.

Merge Details

The released merged model uses checkpoint 7086. The LoRA merge applied the text decoder adapter into BF16 base weights. Vision tower and multimodal projector weights remain base-model weights.

Training Charts

Running With llama.cpp

Base model:

hf download beza4588/TenaOS --local-dir ./models

llama-server \
  -m ./models/gemma-4-E4B-it-BF16.gguf \
  --mmproj ./models/mmproj-gemma-4-E4B-it-bf16.gguf \
  --host 0.0.0.0 \
  --port 8000 \
  -ngl 99 \
  --jinja \
  --alias gemma-4

Merged LoRA model, when using the merged GGUF artifact:

llama-server \
  -m ./models/tenaos-gemma-4-E4B-it-lora-F16.gguf \
  --mmproj ./models/mmproj-tenaos-gemma-4-E4B-it-lora-bf16.gguf \
  --host 0.0.0.0 \
  --port 8000 \
  -ngl 99 \
  --jinja \
  --alias gemma-4

In TenaOS, the Docker image bind-mounts this directory at /models. See scripts/fetch-models.sh.

Intended Use

This model package is intended for the TenaOS local clinical AI runtime. It is not intended to autonomously diagnose, prescribe, or write directly to a medical record. TenaOS uses allow-listed tools, local WHO/MSF and CIEL knowledge bases, deterministic validation, and clinician review.

Limitations

• The adapter is trained for TenaOS workflow traces and task tags. It should be evaluated in the full TenaOS runtime rather than as a generic chat model.
• Workflow-level metrics such as form recall, report correctness, scribe extraction quality, unsupported clinical recommendation rate, citation quality, and CDS grounding should be measured in the full runtime.
• Clinical output remains draft material until reviewed by a qualified clinician.

License

The Gemma model artifacts inherit the Gemma Terms of Use. TenaOS packaging and application code are released separately under the Apache 2.0 license.