Translation
Transformers
Safetensors
Kannada
English
controlmt
text2text-generation
machine-translation
kannada
english
indic
low-resource
code-mix
encoder-decoder
custom_code
Eval Results (legacy)
Instructions to use anandkaman/controlmt-v2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anandkaman/controlmt-v2.3 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="anandkaman/controlmt-v2.3", trust_remote_code=True)# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("anandkaman/controlmt-v2.3", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
docs: move to release/docs/, neutralize internal-doc refs, add prerequisites + data source links + cost estimate + first-week checklist + architecture diagram, rewrite working-with-claude opening in natural voice
31eae1a verified | # ControlMT documentation — the journey + the playbook | |
| > A first-person record of building a 139M-parameter Kannada↔English translator from scratch, solo, on one consumer GPU, with an AI assistant as a collaborator. For ML engineers who want to build something similar. | |
| This folder is six self-contained docs. Each one stands alone — read whichever match your interest, in any order. They cross-reference each other so you don't have to read all of them to learn one thing. | |
| --- | |
| ## Pick what you want to read | |
| | If you want to … | Read this | | |
| |---|---| | |
| | **Get the highest-density version of everything I learned** | [`top-lessons.md`](top-lessons.md) — 10 lessons, one paragraph each | | |
| | **Read the full chronological story (v1 → v2.3 public release)** | [`the-journey.md`](the-journey.md) — what I built, when, why, what surprised me | | |
| | **Skip my mistakes — what I tried that didn't work** | [`what-didnt-work.md`](what-didnt-work.md) — 8 failed experiments + root-cause analysis | | |
| | **Just the concrete recipes (no theory)** | [`how-it-was-built.md`](how-it-was-built.md) — data filtering, training schedule, eval, deployment | | |
| | **Learn the AI-assistant collaboration patterns** | [`working-with-claude.md`](working-with-claude.md) — memory rules, background tasks, what to delegate | | |
| | **Find anything in the repo** | [`repo-map.md`](repo-map.md) — folder layout + file conventions | | |
| --- | |
| ## Reading paths by use case | |
| **"I have 10 minutes."** | |
| Read [`top-lessons.md`](top-lessons.md). It's the synthesis. Each lesson links to the doc that explains it in depth. | |
| **"I want to build a small specialized translation model."** | |
| 1. [`how-it-was-built.md`](how-it-was-built.md) — concrete pipeline | |
| 2. [`what-didnt-work.md`](what-didnt-work.md) — skip my dead ends | |
| 3. [`top-lessons.md`](top-lessons.md) — the higher-level patterns | |
| **"I want to understand the journey + decisions."** | |
| 1. [`the-journey.md`](the-journey.md) — narrative | |
| 2. [`what-didnt-work.md`](what-didnt-work.md) — companion (the failures) | |
| 3. [`top-lessons.md`](top-lessons.md) — synthesis | |
| **"I'm a solo developer using AI assistants and want to do this kind of work."** | |
| 1. [`working-with-claude.md`](working-with-claude.md) — the collaboration playbook | |
| 2. [`the-journey.md`](the-journey.md) — applied case study (this project) | |
| 3. [`top-lessons.md`](top-lessons.md) — synthesis | |
| **"I'm trying to navigate the codebase."** | |
| 1. [`repo-map.md`](repo-map.md) — folder + file map | |
| 2. [`how-it-was-built.md`](how-it-was-built.md) — what each pipeline stage does | |
| 3. Then dive into the GitHub repo at [github.com/anandkaman/ControlMT](https://github.com/anandkaman/ControlMT) — the actual training scripts, model code, and pipeline live there | |
| **"I'm interested specifically in the model itself, not the project around it."** | |
| You probably want the public model card instead — [`../release/README.md`](../release/README.md) — which has the FLORES benchmark scores, intended use, limitations, citation info. Then come back here for the journey context. | |
| --- | |
| ## What's NOT in this folder | |
| - **Live training instructions or hyperparameters** — those are in [`../TRAINING_GUIDE.md`](../TRAINING_GUIDE.md) (the public methodology doc) | |
| - **Deployment recipes (CPU / GPU / Docker / SDK)** — those are in [`../release/DEPLOYMENT.md`](../release/DEPLOYMENT.md) | |
| - **Strategic positioning vs competitors** — in my private working directory | |
| - **Old design docs from earlier phases** — preserved in [`archive/`](archive/) (10 historical docs: original architecture, training-log-v1, dataset-migration plans, etc.). These are referenced from the new docs but kept frozen. | |
| --- | |
| ## A note on voice | |
| These docs are written in first person, in my voice (Anand Kaman). I describe my decisions, my mistakes, what surprised me. That makes them more readable but also more vulnerable — I'm naming specific things I got wrong and what I'd do differently. | |
| If you're an ML engineer with similar constraints (single GPU, solo, no funding), I hope the failures are at least as useful as the successes. The dead ends saved me weeks. Maybe this writeup saves you some. | |
| --- | |
| ## Found a problem with these docs? | |
| The repo is public at [github.com/anandkaman/ControlMT](https://github.com/anandkaman/ControlMT). Open an issue or PR. The docs in this folder are versioned alongside the model itself — when v2.4 ships, I'll update the journey to extend through v2.4 and re-publish. | |