Add Qwen3.6-27B Core AI card, license, config

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+gpu-pipelined/qwen3_6_35b_a3b_decode_int8hu_block32_sym/qwen3_6_35b_a3b_decode_int8hu_block32_sym.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text
+gpu-pipelined/qwen3_6_35b_a3b_decode_int8hu_block32_sym/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+*.mlirb filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

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+---
+license: apache-2.0
+base_model:
+  - Qwen/Qwen3.6-27B
+tags:
+  - core-ai
+  - coreai
+  - apple
+  - qwen3_5
+  - dense
+  - text-generation
+language:
+  - en
+pipeline_tag: text-generation
+library_name: coreai
+---
+
+# Qwen3.6-27B — Core AI (Apple) bundle
+
+The **dense Mac-class companion** to [Qwen3.6-35B-A3B-CoreAI](https://huggingface.co/mlboydaisuke/Qwen3.6-35B-A3B-CoreAI),
+converted for Apple's **Core AI** runtime (iOS/macOS 26+ successor to Core ML).
+Source: [`Qwen/Qwen3.6-27B`](https://huggingface.co/Qwen/Qwen3.6-27B) (text decoder).
+
+Where the 35B-A3B is a sparse MoE, the 27B is the **same Qwen3.5 hybrid decoder run dense** —
+no experts, no router, just the proven token mixers at scale. 64 layers on a 3:1 interleave of
+**GatedDeltaNet** linear-attention mixers and **gated full attention**:
+
+- full attention: head_dim 256, **GQA 24 query / 4 KV heads**, partial mRoPE θ=1e7, swish output gate;
+- linear (GatedDeltaNet): **48 value heads over 16 key heads** (GVA — each k/q head shared across
+  *three* value heads, vs the 35B's two);
+- every FFN a dense `MLP(17408)` (no MoE); untied 248320-vocab `lm_head`.
+
+**27B parameters, all dense → the entire model is read per token.** Unlike the 35B-A3B
+(≈3B active), there is no sparsity to hide behind: this is a true 27B-class decode — the quality
+of a large dense model at the memory-bandwidth speed that implies on a Mac.
+
+## Bundle
+
+`gpu-pipelined/qwen3_6_27b_decode_int8hu_block32_sym/` — a ready-to-run **Core AI LanguageBundle**
+(`.aimodel` + `metadata.json` + tokenizer), **28 GB**, decode-only loop-free for Apple's pipelined
+GPU engine. int8 linear per-block-32 weights + an absmax int8 untied head (`int8hu --head-sym`).
+
+## Measured (macOS 27 beta, M4 Max 128 GB, `llm-benchmark`, `COREAI_CHUNK_THRESHOLD=1`)
+
+| metric | value |
+|---|---|
+| decode | **15.9 tok/s** |
+| prefill | 15.8 tok/s (pipelined S=1) |
+| bundle | 28 GB |
+| numerics | int8 == full precision at every confident position (teacher-forced vs bf16 HF oracle) |
+
+**Numerics in full** — 27B fp32 would need ~111 GB RAM, so the oracle is the checkpoint's native
+**bf16**; the gate is teacher-forced single-step argmax under an oracle-margin≥0.1 rule. The result
+is cleaner than the 35B-A3B's: **int8 adds zero confident-margin flips over full precision.** Both
+`int8hu` and an fp16 control score 15/16 vs the bf16 oracle and fail the *same* position (margin
+0.50), where fp16 flips **byte-identically to int8** — a bf16-oracle-resolution artifact, not an
+int8 defect. The only int8-vs-fp16 difference anywhere is one sub-0.1-margin tie.
+
+**Speed is bandwidth-bound, as a dense 27B at int8 must be:** ~28 GB/token → 15.9 tok/s is ~87 % of
+the M4 Max memory-bandwidth ceiling. (The 35B-A3B decodes *faster* than this despite more
+parameters, because only ~3B are active per token — that is the MoE's whole point.)
+
+**int4 is a size/speed option, not the quality ship.** A linear int4 bundle (`int4lin`, ~14 GB,
+~2× decode) gates 15/16 too but pays a real cost: it flips a high-confidence position that fp16 and
+int8 both get right, and its per-position cosine is systematically lower. A **mixed-precision**
+middle ground (MLP int4 / attention·GatedDeltaNet·head int8) was tested and rejected — keeping the
+mixers at int8 repairs int4's flip (confirming the attention/GDN path, not the FFN, is the
+4-bit-sensitive part), but the int4 MLP then introduces its *own* confident flip that edge-layer
+int8 cannot fix. So there is no quality-preserving speedup between int8 (clean, 15.9 tok/s) and int4
+(borderline, ~30): **int8 is the quality ship**, int4 the size/speed option, nothing useful between.
+
+**Mac-only:** at 28 GB this is a 64/128 GB-Mac model (far past the iPhone memory limit).
+
+## How to run
+
+This is a Core AI bundle for Apple's pipelined LLM engine (`llm-benchmark` / `llm-runner` from
+`apple/coreai-models`, plus the community pipelined extra-states patch). The conversion recipe and
+the full write-up live in the community zoo:
+[github.com/john-rocky/coreai-model-zoo](https://github.com/john-rocky/coreai-model-zoo)
+(`zoo/qwen3.6-27b.md`). The decoder reuses the shared `qwen3_5.py` overlay directly — no MoE files.
+
+```bash
+COREAI_CHUNK_THRESHOLD=1 llm-benchmark \
+    --model gpu-pipelined/qwen3_6_27b_decode_int8hu_block32_sym -p 64 -g 128 -n 3
+```

config.json

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+{
+    "architectures": [
+        "Qwen3_5ForConditionalGeneration"
+    ],
+    "image_token_id": 248056,
+    "language_model_only": false,
+    "model_type": "qwen3_5",
+    "text_config": {
+        "attention_bias": false,
+        "attention_dropout": 0.0,
+        "attn_output_gate": true,
+        "bos_token_id": 248044,
+        "dtype": "bfloat16",
+        "eos_token_id": 248044,
+        "full_attention_interval": 4,
+        "head_dim": 256,
+        "hidden_act": "silu",
+        "hidden_size": 5120,
+        "initializer_range": 0.02,
+        "intermediate_size": 17408,
+        "layer_types": [
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention"
+        ],
+        "linear_conv_kernel_dim": 4,
+        "linear_key_head_dim": 128,
+        "linear_num_key_heads": 16,
+        "linear_num_value_heads": 48,
+        "linear_value_head_dim": 128,
+        "mamba_ssm_dtype": "float32",
+        "max_position_embeddings": 262144,
+        "model_type": "qwen3_5_text",
+        "mtp_num_hidden_layers": 1,
+        "mtp_use_dedicated_embeddings": false,
+        "num_attention_heads": 24,
+        "num_hidden_layers": 64,
+        "num_key_value_heads": 4,
+        "output_gate_type": "swish",
+        "pad_token_id": null,
+        "partial_rotary_factor": 0.25,
+        "rms_norm_eps": 1e-06,
+        "rope_parameters": {
+            "mrope_interleaved": true,
+            "mrope_section": [
+                11,
+                11,
+                10
+            ],
+            "partial_rotary_factor": 0.25,
+            "rope_theta": 10000000,
+            "rope_type": "default"
+        },
+        "tie_word_embeddings": false,
+        "use_cache": true,
+        "vocab_size": 248320
+    },
+    "tie_word_embeddings": false,
+    "transformers_version": "4.57.1",
+    "video_token_id": 248057,
+    "vision_config": {
+        "deepstack_visual_indexes": [],
+        "depth": 27,
+        "hidden_act": "gelu_pytorch_tanh",
+        "hidden_size": 1152,
+        "in_channels": 3,
+        "initializer_range": 0.02,
+        "intermediate_size": 4304,
+        "model_type": "qwen3_5",
+        "num_heads": 16,
+        "num_position_embeddings": 2304,
+        "out_hidden_size": 5120,
+        "patch_size": 16,
+        "spatial_merge_size": 2,
+        "temporal_patch_size": 2
+    },
+    "vision_end_token_id": 248054,
+    "vision_start_token_id": 248053
+}