---
library_name: needle-point
license: apache-2.0
pipeline_tag: text-generation
tags:
  - tool-calling
  - function-calling
  - constrained-decoding
  - automaticity
---

# Needle Automaticity V10

Needle Automaticity V10 is a compact bounded tool-calling model for the Needle
Point runtime. It is intended for fast sensing, finite one-off actions, and
simple expression—not open-ended planning or unconstrained tool arguments.

The model uses 36 allowed capabilities with closed enum, const, boolean, and
finite-array argument domains. Open-ended capabilities are excluded.

## Training

The checkpoint was trained with upstream JAX Needle pinned to commit
`ffb1c5144c5a16cb8ec650dbc8a6f6fd3854f8f2` and converted for Needle Point.
Training used a 1,767-row JEV-assisted corpus. JEV was used to audit labels and
identify hard examples for additional exposure; teacher responses were not
copied into the training labels. All action disagreements were manually
reviewed and canonical gold labels were retained.

## Held-out evaluation

Acceptance was run through Needle Point strict constrained decoding on a
family-isolated 360-case benchmark:

| Lane | Exact match |
|---|---:|
| Action/tool call | 215/216 (99.5370%) |
| Hard no-tool boundary | 144/144 (100%) |
| Combined | 359/360 (99.7222%) |

All outputs parsed and validated, no boundary case overcalled, and no
constraint fallback occurred.

A later 100-case fresh adversarial holdout produced 56% exact match: 53.33% on
60 action cases and 60% on 40 hard no-tool cases, with 98% valid outputs. This
is the more conservative estimate of robustness to novel adversarial wording;
the 99.72% result is specific to the curated V10 held-out distribution.
The unassisted gold checkpoint scored 58% on the same fresh holdout (36.67%
action, 90% no-tool), showing that assisted hard-example weighting traded
higher action recall for substantially lower boundary precision.

## Files and integrity

- `model.safetensors`:
  `7bf8417fb2dc6ff03f33549e959e6aa4dd06898ab4a3d7d160f63bee1e4693af`
- `config.json`:
  `bb90a73f354403511e0c83df8a5962e04752be831af386074080423aec34695e`
- `tokenizer.model`:
  `0823f5b9133c68a8140addc5d7a425fa9119c4c8cb4a550363b4bffa4ba1c8c7`

Use the strict Needle Point runtime and provide a finite candidate-tool set for
each request. The model is not intended to authorize tools or bypass runtime
policy checks.