| --- |
| 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. |
|
|
