README.md

metadata

title: amx-reasoning-v1-instruct QA
emoji: 🧮
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 6.26.0
app_file: app.py
short_description: Passage QA with a 7.5M-param CPU-trained LM
python_version: '3.12'
startup_duration_timeout: 30m
license: apache-2.0
models:
  - gdiamos/amx-reasoning-v1-instruct

amx-reasoning-v1-instruct — passage QA

Demo for gdiamos/amx-reasoning-v1-instruct: a 7,492,448-parameter causal LM (3,315,552 active per token) trained end to end on a single Intel Emerald Rapids CPU core. Give it a passage and a question and it extracts a one-or-two-word answer.

The demo runs the model's own reference path, unmodified:

• the m2r package that trained it (the architecture is not a transformers one — AutoModelForCausalLM will not load it),
• render_prompt(..., thinking=False) for the exact prompt format,
• greedy argmax decoding, stopped on the <SPECIAL_12> end-of-turn token,
• the 800-token vocabulary mask from generation.json applied before the argmax (required for correct output; the demo exposes a toggle so you can see what happens without it).

Inference runs on CPU, which is the hardware the model was designed and trained for — a forward pass at these dimensions is a few GFLOP.

Example attribution

Example passages are drawn from the datasets the model card reports on: DROP and SQuAD v2 (CC BY-SA 4.0), and databricks-dolly-15k (CC BY-SA 3.0). The first example is the model repo's own example.py.