---
license: mit
license_link: https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B/blob/main/LICENSE
base_model: deepreinforce-ai/Ornith-1.0-9B
pipeline_tag: text-generation
library_name: core-ai
tags:
- core-ai
- coreml
- apple
- on-device
- metal
- code
- agent
---

# Ornith-1.0-9B — Core AI (agentic coder, 48–59 tok/s on M4 Max)

Apple **Core AI** (`.aimodel`) conversion of
[deepreinforce-ai/Ornith-1.0-9B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B)
(text decoder): DeepReinforce's **self-scaffolding agentic-coding** model — trained to jointly
solve coding tasks and construct the orchestration scaffold that guides the solution.
Architecturally a **Qwen3.5 hybrid decoder** (`model_type qwen3_5`): 32 layers on a 3:1
interleave of GatedDeltaNet linear-attention mixers and gated full attention, untied
248320-vocab head. Runs fully on-device on Apple silicon via Apple's `coreai-pipelined`
GPU engine.

Part of the community Core AI model zoo: **https://github.com/john-rocky/coreai-model-zoo**
(full card: [`zoo/ornith-1.0-9b.md`](https://github.com/john-rocky/coreai-model-zoo/blob/main/zoo/ornith-1.0-9b.md)).

## Bundles

| bundle | size | M4 Max decode / prefill | quality |
|---|---:|---:|---|
| `gpu-pipelined/ornith_1_0_9b_decode_int8hu_block32_sym/` (**ship**) | 9.8 GB | **48.3 / 48.5 tok/s** | teacher-forced eager gate **24/24 exact** vs fp32 HF oracle; engine greedy **12/12 token-exact** |
| `gpu-pipelined/ornith_1_0_9b_decode_int4lin/` (speed option) | 7.5 GB | **58.9 / 59.0 tok/s** (+22%) | ALSO gates **24/24 exact** + engine **12/12** — the first clean int4 PTQ pass in the Qwen3.5 family (verified at short context; int8 carries the quality claim at long context) |

Both are decode-only loop-free S=1 LanguageBundles (`max_ctx` 8192, tokenizer + chat
template embedded), gated against a margin-validated fp32 HF oracle (min top-2 margin
0.205; the eager fp16 baseline is also 24/24, so int8/int4 add **zero** flips).

Recipe: linear int8/int4 per-block-32 body + absmax `symmetric` per-block-32 int8 on the
untied big-vocab head (int8hu). Conversion is the zoo's stock Qwen3.5 script with nothing
but an `--hf-id` swap — see
[`conversion/README.md`](https://github.com/john-rocky/coreai-model-zoo/blob/main/conversion/README.md).

## Run it (macOS)

Easiest: **CoreAIChatMac** (the zoo's Mac chat app —
[`apps/CoreAIChatMac`](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps/CoreAIChatMac))
downloads this repo in-app: pick **Ornith-1.0-9B** in the Downloads panel.

CLI (needs the zoo's `coreai-models` checkout + the pipelined extra-states patch):

```bash
COREAI_CHUNK_THRESHOLD=1 llm-benchmark \
    --model ornith_1_0_9b_decode_int8hu_block32_sym -p 128 -g 256 -n 3
COREAI_CHUNK_THRESHOLD=1 llm-runner \
    --model ornith_1_0_9b_decode_int8hu_block32_sym \
    --prompt "Write a rate limiter in Swift." --sampling-strategy greedy \
    --warmup exact --warmup-length 1
```

Notes: `COREAI_CHUNK_THRESHOLD=1` before engine creation; never `engine.warmup()` on an
S=1 bundle; Release builds only. Details + every trap:
[`knowledge/pipelined-engine.md`](https://github.com/john-rocky/coreai-model-zoo/blob/main/knowledge/pipelined-engine.md).

## iPhone

Not this one (yet): int8 at 9.8 GB exceeds the entitled jetsam ceiling (~6.4 GB on an
iPhone 17 Pro). The arithmetic route is int4 body + int8 head (≈6.5 GB) or an in-graph
int8 embed table (≈5.5 GB) — tracked in the zoo card.

## License

MIT — as declared by the upstream model card
([deepreinforce-ai/Ornith-1.0-9B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B);
the upstream repo ships no LICENSE file, so none is mirrored here). Conversion scripts
and harness: see the zoo repo.