Thomson-1.0-Small

About

What Thomson-1.0-Small is

Thomson-1.0-Small [!Note] This repository contains model weights and configuration files in the Hugging Face Transformers format. For more details, please refer to our technical report, Thomson: Continual Learning of Frontier Models for SovereignAI. Thomson-1.0-Small is a frontier Foundation Model of high proficiency across a wide range of specialised and general-purpose domains as well as practical deployment settings. It is the open-weight member of the Thomson-1.0 family, developed within a Continual Learning paradigm with a deliberate focus on economically impactful, high-stakes professional work across legal, tax, and journalism domains. It is obtained by repurposing the open-weight Qwen3.6-35B-A3B model and substantially improving it on a wide range of performance domains. Thomson-1.0 Highlights The development of frontier models is commonly perceived to be in the exclusive remit of a small number of heavily funded players. Thomson demonstrates that frontier performance can instead be achieved by a wide range of institutions through Continual Learning on readily available open-weight models: - Continual Learning: as opposed to limited approaches such as small-scale fine-tuning, prompt engineering, or tool-augmentation on a frozen model, the pipeline delivers distinct improvements across a wide range of capabilities, including those not explicitly targeted, while almost eliminating the forgetting problem common to narrow domain adaptation. - High-stakes professional work: an enhanced focus on legal, tax, and journalism — domains commonly predicted to undergo large productivity improvements through AI, combining the formality and rigorous reasoning of technical fields with the nuance and tolerance for uncertainty of the humanities. - Value sovereignty: alignment determines which values a model expresses and how it handles questions on which credible narratives diverge. Here it targets the Public AI Constitution, an openly developed document that permits free use and modification, rather than a proprietary value system, and is applied at two points: Constitutional DPO during value re-alignment, and a constitutional conformance reward during reinforcement lear

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys thomson-1-0-small for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (thomson-1-0-small below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"thomson-1-0-small","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Papers

Licence

Commercial licence needed

Sources

Weights & code

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