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linked in READMEs
Jul 23, 2026
updated
README
Nanbeige4.2-3B — Core AI INT8
INT8 weight-quantized Core AI LanguageBundle converted from Nanbeige/Nanbeige4.2-3B, pinned to source revision 5ff54fb7ed86ce8e216d78bff5417ab9981de3d4.
This is a Core AI .aimodel bundle for Apple silicon. It is not a Transformers, GGUF, or MLX checkpoint.
Ported by Vadim Smirnov (@ukint-vs). Source, reproducible conversion recipe, parity gates, and the optimization report are tracked in john-rocky/coreai-model-zoo#5.
Format
| Property | Value |
|---|---|
| Published variant | INT8 only |
| Body weights | int8, per-block 32 (int8hu) |
| Untied LM head | symmetric absmax int8, per-block 32 (--head-sym) |
| Activations / KV cache | fp16 |
| Decode input | static sequence length 1 (--static-ids) |
| Bundle size | approximately 4.59 GiB |
| Verified context | 4,096 tokens |
The int4 and mixed int4/int8 candidates failed the reasoning quality gate and are intentionally not published.
Architecture
Nanbeige4.2 is a looped Llama: 22 physical transformer blocks execute twice, with RMS normalization after each pass and independent KV history. The bundle therefore has 44 executed/cache layers while storing and quantizing one copy of the 22 physical blocks.
Bundle
The complete LanguageBundle is at:
gpu-pipelined/
nanbeige4_2_3b_decode_int8hu_block32_sym_s1/
metadata.json
nanbeige4_2_3b_decode_int8hu_block32_sym_s1.aimodel/
tokenizer/
The embedded tokenizer includes the vendor chat_template.jinja with thinking enabled and disabled modes. The .aimodel producer is coreai-core 1.0.0b2.
Verification
- Official checkpoint vs Core AI authoring: float32 full and cached logits pass
rtol=1e-4,atol=1e-4; identical 32-token greedy continuation. - Quantized Core AI engine: token-exact vs fp32 on 24-token Paris, 16-token freezing-point, and 64-token decimal-reasoning gates.
- M4 Max Release benchmark, prompt 128 / generation 256 / 3 runs: 47.37 prefill / 46.35 decode tok/s.
- 4,096-token boundary: 29.83 prefill / 32.80 decode tok/s, 9.17 GiB peak RSS, zero swaps.
- iPhone
h18pdevice acceptance is pending; no iPhone performance claim is made.
License
The source model and converted weights are distributed under Apache-2.0. Conversion code in coreai-model-zoo is BSD-3-Clause.
Contributors
5 commits
Similar repos
1
stars
5
commits
1
linked in READMEs
Jul 23, 2026
updated
README
Nanbeige4.2-3B — Core AI INT8
INT8 weight-quantized Core AI LanguageBundle converted from Nanbeige/Nanbeige4.2-3B, pinned to source revision 5ff54fb7ed86ce8e216d78bff5417ab9981de3d4.
This is a Core AI .aimodel bundle for Apple silicon. It is not a Transformers, GGUF, or MLX checkpoint.
Ported by Vadim Smirnov (@ukint-vs). Source, reproducible conversion recipe, parity gates, and the optimization report are tracked in john-rocky/coreai-model-zoo#5.
Format
| Property | Value |
|---|---|
| Published variant | INT8 only |
| Body weights | int8, per-block 32 (int8hu) |
| Untied LM head | symmetric absmax int8, per-block 32 (--head-sym) |
| Activations / KV cache | fp16 |
| Decode input | static sequence length 1 (--static-ids) |
| Bundle size | approximately 4.59 GiB |
| Verified context | 4,096 tokens |
The int4 and mixed int4/int8 candidates failed the reasoning quality gate and are intentionally not published.
Architecture
Nanbeige4.2 is a looped Llama: 22 physical transformer blocks execute twice, with RMS normalization after each pass and independent KV history. The bundle therefore has 44 executed/cache layers while storing and quantizing one copy of the 22 physical blocks.
Bundle
The complete LanguageBundle is at:
gpu-pipelined/
nanbeige4_2_3b_decode_int8hu_block32_sym_s1/
metadata.json
nanbeige4_2_3b_decode_int8hu_block32_sym_s1.aimodel/
tokenizer/
The embedded tokenizer includes the vendor chat_template.jinja with thinking enabled and disabled modes. The .aimodel producer is coreai-core 1.0.0b2.
Verification
- Official checkpoint vs Core AI authoring: float32 full and cached logits pass
rtol=1e-4,atol=1e-4; identical 32-token greedy continuation. - Quantized Core AI engine: token-exact vs fp32 on 24-token Paris, 16-token freezing-point, and 64-token decimal-reasoning gates.
- M4 Max Release benchmark, prompt 128 / generation 256 / 3 runs: 47.37 prefill / 46.35 decode tok/s.
- 4,096-token boundary: 29.83 prefill / 32.80 decode tok/s, 9.17 GiB peak RSS, zero swaps.
- iPhone
h18pdevice acceptance is pending; no iPhone performance claim is made.
License
The source model and converted weights are distributed under Apache-2.0. Conversion code in coreai-model-zoo is BSD-3-Clause.
Similar repos
Linked in READMEs
Contributors
5 commits