Addis AI

Addis AI

Open Research at Addis AI

Two open projects examine how continued pretraining, instruction tuning and domain specialization change capability and alignment in Amharic. Review the agriculture and health scopes, budgets, methods and progress below.

Projects

Current research

Research groundwork underwayAmharic phase

Evaluating alignment and reliability in Amharic agricultural AI

This project measures how Amharic language adaptation and agricultural fine-tuning change model capability, refusal, uncertainty and locally relevant advice.

External funding toward this phase

$0

of $27,500 full target

Groundwork already funded and underway. Addis AI has funded the underlying model research and infrastructure; external funding expands this evaluation phase.

$0Minimum useful scope $14,000
Minimum scope
$14,000
Full target
$27,500
Minimum study
Approximately 8 weeks
Full study
Approximately 10 to 12 weeks
Read project scope
Research groundwork underwayAmharic phase

Evaluating alignment and safety in Amharic health AI

This project measures how Amharic language adaptation and health fine-tuning change safety behavior, factuality, uncertainty and escalation.

External funding toward this phase

$0

of $33,000 full target

Groundwork already funded and underway. Addis AI has funded the underlying model research and infrastructure; external funding expands this evaluation phase.

$0Minimum useful scope $18,000
Minimum scope
$18,000
Full target
$33,000
Minimum study
Approximately 10 weeks
Full study
Approximately 12 to 14 weeks
Read project scope

Core question

How does each adaptation stage change capability and alignment in Amharic?

The same evaluation suite follows the model through four checkpoints. This separates changes from language learning, instruction tuning and domain specialization.

  1. Stage 1

    Aligned instruction-tuned open checkpoint

    Start from an instruction-tuned open model with an existing safety and instruction-following baseline. The exact checkpoint and configuration will be selected and frozen in the public protocol before the main evaluation begins.

  2. Stage 2

    Amharic language adaptation

    Apply the frozen Amharic language-adaptation configuration, then measure capability, factuality, language, local relevance, safety and alignment behavior again.

  3. Stage 3

    Broad Amharic instruction tuning

    Teach general Amharic instruction following, then repeat the same evaluation suite.

  4. Stage 4

    Domain specialization

    Apply agriculture or health supervised fine-tuning, then compare the change from every earlier stage.

At every stage: capability and safety evaluation, failure analysis, mitigation where indicated, and open results and artifacts.

What Open Research is

Public research scopes with methods, budgets and updates.

Each project has a defined question, evaluation plan, funding target, minimum scope and release plan. The project pages show what exists, what still needs funding and what the work can establish.

These projects use agriculture and health as experimental settings. The broader contribution is evidence about how alignment changes during low-resource language adaptation.

What already exists

Public models, tools and benchmark methods

Addis AI has previously carried out vocabulary extension, continued pretraining and supervised fine-tuning for Amharic.

That work informs the adaptation method, but the agriculture and health studies use a separate experimental configuration that will be frozen in a public protocol.

The research scopes and funding records live on addisai.ch. Public models, datasets, tools and benchmarks remain on Addis AI's open-source sites.

Research portfolio

Released work and research manuscripts

Open models, public benchmark results and research manuscripts from Addis AI. Release and manuscript status are reported separately.

Model released
Funded by Addis AIApache-2.0

Qwen3-ASR-0.6B Amharic, broader mixed

Open Amharic speech-recognition checkpoint

An open Amharic automatic speech recognition checkpoint adapted from Qwen3-ASR-0.6B with a broader speech-text mixture for stronger generalization beyond the curated training domain.

Public benchmark result: On the public fixed held-out evaluations, the broader mixed checkpoint improved WAXAL word error rate by 0.99 percentage points over the curated variant. The curated checkpoint remained 0.47 points better on its source-matched heldout.

Scope note: The reported benchmarks cover limited held-out corpora and do not represent universal Amharic speech recognition performance.

View model card, benchmarks and demo
Research manuscriptPreview available on request
Funded by Addis AI

Adapting a 4B Language Model to Amharic

Vocabulary extension, continued pretraining and supervised fine-tuning

A technical study of adapting a small open foundation model to Amharic through vocabulary extension, continued pretraining and supervised fine-tuning. The work documents the adaptation pipeline, data-engineering lessons, negative results and evaluation-methodology questions that motivate the next controlled experiments.

Scope note: This prior work informs the adaptation method. The controlled agriculture and health studies described here are separate experiments.

Request research preview
Research manuscriptPreview available on request
Funded by Addis AI

Evaluating a Production Hausa Speech-to-Text System

Accuracy, robustness and latency under domain-specific speech conditions

A research manuscript evaluating a deployed Hausa speech-recognition system with a reproducible benchmark focused on accuracy, robustness and operational performance under clean and noisy conditions.

Scope note: This evaluation manuscript belongs to the broader speech-research portfolio and is not evidence for the agriculture or health alignment studies.

Request research preview

Human and domain evaluation

The main bottleneck is qualified human review.

Automated measures cannot decide whether Amharic is natural, advice fits local conditions, a red flag was missed or uncertainty was stated well. Native speakers and domain reviewers define the scenario set, score outputs and resolve disagreements.

What funding enables

  • Native Amharic evaluators
  • Agronomists and qualified health professionals
  • Scenario creation, contextual tests and preference labels
  • Red-team cases, repeated runs and mitigation tests
  • Reproducibility work and public release

Open-source commitment

Methods and results will be public.

We will publish protocols, taxonomies, scoring code, rubrics, stage comparisons, aggregate and per-slice results, mitigation results, configurations, limitations and reproduction instructions. We will publish null and negative results.

Datasets, checkpoints and source material will be released where privacy, licensing and safety permit.

Research updates

We publish progress, changes in scope, negative results and delays as they happen.

2026-08-31

Agriculture: Project scope published

The public scope, stage comparison, initial budget and planned outputs are available for review.

2026-08-31

Health: Project scope published

The public scope, stage comparison, initial budget and planned outputs are available for review.

Funding transparency

Public totals come from the project ledger.

Funding totals include only cleared contributions and confirmed project-specific grants or sponsorships.

The funding policy covers project use, minimum scope, scope changes, overfunding, unused funds, fees, reconciliation and contributor terms.

Read the funding policy

Institutional contact

Grants, sponsorship, review and collaboration

Research collaborators, grantmakers and institutional sponsors can request a technical research preview or discuss a project.

contact@addisai.ch

This form is for research, grant and sponsorship conversations, not product demo requests.

Future projects

Possible work after the current Amharic phase

These are possible research directions, not funded projects or delivery commitments.

  1. Tigrinya speech recognition
  2. Wolayta speech recognition
  3. Sidama speech recognition
  4. Multilingual speech recognition
  5. Multilingual alignment
  6. Low-resource model adaptation