Publish validated AMOS Core AI conversion with source provenance and runtime adapter

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LICENSE

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README.md

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+---
+license: apache-2.0
+base_model: mukaddamzaid/amos-1-medium
+tags: [coreai, core-ai, apple, aimodel, decisions, vision]
+---
+
+# amos-1-medium — Apple Core AI
+
+Independent AMOS-1 conversion with its checkpoint's **causal full attention** preserved. Text backbone, 255-code readout, vision tower, span head, region head and trained box head execute through Core AI. Original prompt formatting, temperatures and quote decoding are retained. This is a decision model, not a text generator.
+
+## Use
+
+Requires macOS 27 and the Core AI Python runtime. Tested with `coreai-torch==0.4.3`, PyTorch 2.14.0, Transformers 5.19 development, NumPy and Pillow. Exact dependencies are in `requirements.txt`. The converter warns that this PyTorch version exceeds its validated range. Host preprocessing/decoding uses PyTorch tensors; it loads no PyTorch neural weights.
+
+```python
+import asyncio
+from coreai_decoder import AmosDecoder
+
+async def main():
+    m = await AmosDecoder.load(".")
+    print(await m.request({
+        "state": "Please refund the duplicate charge.",
+        "questions": {"refund": {
+            "type": "noul", "instructions": "Is a refund requested?"
+        }}
+    }))
+
+asyncio.run(main())
+```
+
+`predict(state, questions, images=())` takes upstream internal questions (`text`, `options`) and PIL images, including image options. `request()` takes base64 images in the original AMOS format.
+
+## Verified scope and limits
+
+Batch one, **512 combined tokens**, 224×224 images (49 merged tokens), float32 backbone/vision weights with FP32 readout and heads. Requests over capacity raise an error. Original longer contexts, variable image resolution, wide-choice two-pass routing, rotations, thinking, hosted server and explanation endpoints are not implemented. Up to 255 option codes are available subject to token capacity. No iPhone, Neural Engine placement, throughput or upstream benchmark accuracy claim has been verified. Core AI uses default specialization.
+
+The text graph is split into an embedding asset, 32 transformer-layer assets and a final normalization/readout asset. They execute sequentially through Core AI in FP32. Conversion and the independent reference expand only one layer at a time. Gated-delta attention uses functional forward substitution instead of the export-only inverse construction; this matches the original eager triangular solve. The replacement passed 21 independent randomized comparisons and was checked against intermediate outputs from the trained model. Tested on a 64 GiB Mac; no smaller-memory device has been verified.
+
+Nine smoke/parity cases cover all six typed question kinds, quote extraction, Unicode, 24 options, synthetic state images and image options. Tested discrete decisions matched upstream. Maximum measured probability/box-coordinate error: **0.00001425**. Full results are in `validation.json`; these smoke cases do not establish benchmark accuracy. `runtime-smoke.json` verifies inference with PyTorch neural execution disabled.
+
+The primary parity reference executes the pinned checkpoint in FP32. Compared with the original BF16 serving path in 9 additional cases, maximum probability/box error was 0.109300, and 1 cases changed a discrete output. Region confidence can change discontinuously when a box crosses a grid boundary. See `bf16-comparison.json`; this is not bit-identical BF16 serving.
+
+## Attribution and reproducibility
+
+Original model and serving code: Zaid Mukaddam, [mukaddamzaid/amos-1-medium](https://huggingface.co/mukaddamzaid/amos-1-medium). Weights derive from Qwen3.5. Apache-2.0 applies; see `LICENSE` and `SOURCE.json` for the pinned AMOS revision and source hashes. Conversion adds no restrictions. Upstream serving sources are included under `serve/`.
+
+Download the pinned source and reproduce from a directory holding these scripts:
+
+```bash
+hf download mukaddamzaid/amos-1-medium --revision 6b48a91ed940e08c87dcdd29c71c3b256d757ec7 --local-dir upstream/amos-1-medium
+python convert_medium_sharded.py
+python validate_decoder.py amos-1-medium --mode reference
+python validate_decoder.py amos-1-medium --mode coreai
+python smoke_decoder.py amos-1-medium
+```
+
+The two validation processes keep the reference model and Core AI runtime out of memory at the same time. Release integrity is recorded in `SHA256SUMS`.

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+  {
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+    ]
+  },
+  {
+    "case": "image-option",
+    "tokens": 181,
+    "reference_precision": "bfloat16",
+    "upstream": [
+      {
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+        "probs": [
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+          0.030458426102995872
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+        "choice": 0
+      }
+    ]
+  }
+]

bf16-comparison.json

@@ -0,0 +1,315 @@
+[
+  {
+    "case": "refund",
+    "max_probability_or_box_error": 0.0011562108993530273,
+    "discrete_outputs_match": true,
+    "upstream_bf16": [
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+      {
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+    ]
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+  {
+    "case": "span",
+    "max_probability_or_box_error": 0,
+    "discrete_outputs_match": true,
+    "upstream_bf16": [
+      {
+        "type": "span",
+        "start": 55,
+        "end": 63,
+        "text": "INV-2231",
+        "p": 1.0
+      }
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+    "coreai_float32": [
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+        "end": 63,
+        "text": "INV-2231",
+        "p": 1.0
+      }
+    ]
+  },
+  {
+    "case": "score",
+    "max_probability_or_box_error": 0.0032760053873062134,
+    "discrete_outputs_match": true,
+    "upstream_bf16": [
+      {
+        "type": "score",
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+        "expected": 2.7795612774789333
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+    "coreai_float32": [
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+        "choice": 3,
+        "expected": 2.7826968636363745
+      }
+    ]
+  },
+  {
+    "case": "multi",
+    "max_probability_or_box_error": 0.0007659792900085449,
+    "discrete_outputs_match": true,
+    "upstream_bf16": [
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+          0.9263609647750854,
+          0.051194678992033005
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+    ]
+  },
+  {
+    "case": "negative",
+    "max_probability_or_box_error": 0.0009359195828437805,
+    "discrete_outputs_match": true,
+    "upstream_bf16": [
+      {
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+  {
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+    "upstream_bf16": [
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+    ]
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+  {
+    "case": "vision",
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+    "discrete_outputs_match": false,
+    "upstream_bf16": [
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+          0.9735397100448608,
+          0.026460271328687668
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+    ],
+    "coreai_float32": [
+      {
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+        "box": [
+          0.3552,
+          0.0785,
+          0.747,
+          0.9124
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+      {
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+        "probs": [
+          0.974250316619873,
+          0.025749672204256058
+        ],
+        "choice": 0
+      }
+    ]
+  },
+  {
+    "case": "image-option",
+    "max_probability_or_box_error": 0.000291287899017334,
+    "discrete_outputs_match": true,
+    "upstream_bf16": [
+      {
+        "type": "choice",
+        "probs": [
+          0.9695415496826172,
+          0.030458426102995872
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+    ],
+    "coreai_float32": [
+      {
+        "type": "choice",
+        "probs": [
+          0.9698328375816345,
+          0.03016720339655876
+        ],
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+      }
+    ]
+  }
+]

calibration.json

@@ -0,0 +1,7 @@
+{
+ "span": {
+  "cap": 128,
+  "penalty": 1.6,
+  "fitted_on": "held-out quotes: the quote-amounts slice and longquotes clean.jsonl (fit halves), 2026-10-08; cap raised, penalty kept so short quotes are unchanged"
+ }
+}

config.json

@@ -0,0 +1 @@
+{"base": "Qwen/Qwen3.5-9B"}

convert_decoder.py

@@ -0,0 +1,49 @@
+"""Export AMOS Qwen3.5 bidirectional decisions and trained quote/box heads."""
+import argparse,json,math,sys
+from pathlib import Path
+import torch
+from coreai_torch import TorchConverter,get_decomp_table
+from qwen_export_helpers import DecisionVision
+
+class Text(torch.nn.Module):
+ def __init__(self,model,letters):
+  super().__init__();self.language_model=model.model.language_model;self.noncausal=bool(getattr(model.config,'amos_noncausal',False))
+  self.register_buffer('readout',model.get_output_embeddings().weight[letters].detach().float().clone())
+ def forward(self,input_ids,attention_mask,position_ids,visual_embeddings,visual_mask):
+  emb=self.language_model.embed_tokens(input_ids)
+  emb=torch.where(visual_mask,visual_embeddings.to(emb.dtype),emb)
+  mask=attention_mask[:,None,None,:].bool()
+  if not self.noncausal:
+   width=input_ids.shape[1];idx=torch.arange(width,device=input_ids.device);mask=mask&(idx[:,None]>=idx[None,:])[None,None]
+  h=self.language_model(inputs_embeds=emb,attention_mask={'full_attention':mask,'linear_attention':None},position_ids=position_ids,use_cache=False).last_hidden_state
+  return h.float(),h.float()@self.readout.T
+
+class Heads(torch.nn.Module):
+ def __init__(self,heads):super().__init__();self.heads=heads
+ def forward(self,question_hidden,state_hidden,image_hidden):
+  h=self.heads;d=question_hidden.shape[0];dtype=h.span_q.weight.dtype
+  q=question_hidden.to(dtype);s=state_hidden.to(dtype);im=image_hidden.to(dtype)
+  span=torch.einsum('cd,lcd->cl',h.span_q(q).view(2,d),h.span_k(s).view(-1,2,d))/math.sqrt(d)
+  region=h.region_k(im)@h.region_q(q)/math.sqrt(d)
+  box=h.box(q,im,region,7,7) if h.box is not None else torch.zeros(4)
+  return span.float(),region.float(),box.float()
+
+def save(m,args,path):
+ with torch.no_grad():ep=torch.export.export(m,args,strict=False).run_decompositions(get_decomp_table())
+ program=TorchConverter().add_exported_program(ep).to_coreai();program.save_asset(path)
+ return list(ep.graph_signature.user_outputs)
+
+def main():
+ p=argparse.ArgumentParser();p.add_argument('name');p.add_argument('--length',type=int,default=512);p.add_argument('--precision',default='bfloat16');a=p.parse_args()
+ source=Path('upstream')/a.name;out=Path('bundles')/(a.name+'-CoreAI');out.mkdir(parents=True,exist_ok=True)
+ sys.path.insert(0,str((source/'serve').resolve()))
+ from amos.decoder import load_model,load_heads
+ torch.set_num_threads(4);model,codec=load_model(str(source/'model'),'cpu',dtype=getattr(torch,a.precision));model.eval();heads=load_heads(source/'model',model,codec)
+ d=model.config.text_config.hidden_size;L=a.length
+ inputs=(torch.full((1,L),codec.pad,dtype=torch.int32),torch.ones(1,L,dtype=torch.int32),torch.arange(L,dtype=torch.int32).view(1,1,-1).expand(4,1,-1),torch.zeros(1,L,d),torch.zeros(1,L,1,dtype=torch.bool))
+ print('Exporting text',flush=True);outputs=save(Text(model,codec.letters).eval(),inputs,out/'text.aimodel')
+ print('Exporting vision',flush=True);visual=model.model.visual;vo=save(DecisionVision(visual).eval(),(torch.zeros(196,1536),),out/'vision.aimodel')
+ print('Exporting heads',flush=True);ho=save(Heads(heads).eval(),(torch.zeros(d),torch.zeros(L,d),torch.zeros(49,d)),out/'heads.aimodel')
+ (out/'manifest.json').write_text(json.dumps({'sequence_length':L,'hidden_size':d,'outputs':outputs,'vision_output':vo[0],'head_outputs':ho,'precision':a.precision,'echo':codec.echo,'kinds':heads.kinds,'keep':heads.keep,'has_box':heads.box is not None,'image_side':224,'noncausal':bool(getattr(model.config,'amos_noncausal',False))},indent=2))
+ print('Saved',flush=True)
+if __name__=='__main__':main()

convert_medium_sharded.py

@@ -0,0 +1,54 @@
+"""Export FP32 AMOS medium one component at a time to bound conversion memory."""
+import gc,json,sys
+from pathlib import Path
+import torch
+from convert_decoder import save,Heads
+from qwen_export_helpers import DecisionVision
+
+class Embedding(torch.nn.Module):
+ def __init__(self,decoder):super().__init__();self.embed_tokens=decoder.embed_tokens;self.rotary=decoder.rotary_emb
+ def forward(self,input_ids,position_ids,visual_embeddings,visual_mask):
+  h=torch.where(visual_mask,visual_embeddings,self.embed_tokens(input_ids))
+  cos,sin=self.rotary(h,position_ids[1:]);return h,cos,sin
+
+class Layer(torch.nn.Module):
+ def __init__(self,layer):super().__init__();self.layer=layer
+ def forward(self,hidden_states,cos,sin,position_ids,attention_mask):
+  return self.layer(hidden_states,position_embeddings=(cos,sin),position_ids=position_ids,attention_mask=attention_mask if self.layer.block_type=='full_attention' else None,use_cache=False)
+
+class Finish(torch.nn.Module):
+ def __init__(self,model,letters):
+  super().__init__();self.norm=model.model.language_model.norm
+  self.register_buffer('readout',model.get_output_embeddings().weight[letters].detach().float().clone())
+ def forward(self,hidden_states):
+  h=self.norm(hidden_states);return h,h@self.readout.T
+
+def main():
+ source=Path('upstream/amos-1-medium');root=Path('bundles/amos-1-medium-CoreAI');root.mkdir(exist_ok=True,parents=True)
+ sys.path.insert(0,str((source/'serve').resolve()))
+ from amos.decoder import load_model,load_heads
+ torch.set_num_threads(4)
+ m,codec=load_model(str(source/'model'),'cpu',dtype=torch.bfloat16);m.eval();heads=load_heads(source/'model',m,codec)
+ from stable_delta import install
+ install()
+ decoder=m.model.language_model;L=512;d=decoder.config.hidden_size
+ manifest={'sequence_length':L,'hidden_size':d,'precision':'float32','vision_precision':'float32','echo':codec.echo,'kinds':heads.kinds,'keep':heads.keep,'has_box':heads.box is not None,'image_side':224,'noncausal':False,'text_layout':'layer-sharded','layers':[]}
+ path=root/'embedding.aimodel'
+ decoder.embed_tokens.float();emb=Embedding(decoder).eval()
+ args=(torch.ones(1,L,dtype=torch.int32),torch.arange(L,dtype=torch.int32).view(1,1,-1).expand(4,1,-1),torch.zeros(1,L,d),torch.zeros(1,L,1,dtype=torch.bool))
+ with torch.no_grad():_,cos,sin=emb(*args)
+ print('Embedding',flush=True);manifest['embedding_outputs']=save(emb,args,path)
+ decoder.embed_tokens.to('meta');del emb;gc.collect()
+ layer_args=(torch.zeros(1,L,d),cos,sin,torch.arange(L,dtype=torch.int32)[None],torch.ones(1,1,L,L,dtype=torch.bool).tril())
+ for i,layer in enumerate(decoder.layers):
+  name=f'layer-{i:02}.aimodel';print(name,flush=True)
+  layer.float().eval();outputs=save(Layer(layer).eval(),layer_args,root/name)
+  manifest['layers'].append({'asset':name,'output':outputs[0],'kind':layer.block_type})
+  layer.to('meta');gc.collect()
+ print('Finish',flush=True);decoder.norm.float();manifest['outputs']=save(Finish(m,codec.letters).eval(),(torch.zeros(1,L,d),),root/'finish.aimodel')
+ m.get_output_embeddings().to('meta');decoder.norm.to('meta');gc.collect()
+ print('Vision',flush=True);m.model.visual.float();manifest['vision_output']=save(DecisionVision(m.model.visual).eval(),(torch.zeros(196,1536),),root/'vision.aimodel')[0]
+ m.model.visual.to('meta');gc.collect()
+ print('Heads',flush=True);manifest['head_outputs']=save(Heads(heads).eval(),(torch.zeros(d),torch.zeros(L,d),torch.zeros(49,d)),root/'heads.aimodel')
+ (root/'manifest.json').write_text(json.dumps(manifest,indent=2));print('Saved FP32 sharded model',flush=True)
+if __name__=='__main__':main()

coreai_decoder.py

@@ -0,0 +1,100 @@
+"""AMOS Qwen3.5 Core AI adapter. No PyTorch neural model is loaded."""
+import json,sys
+from pathlib import Path
+import numpy as np
+import torch
+from coreai.runtime import AIModel,NDArray,SpecializationOptions
+from transformers import AutoTokenizer,Qwen2VLImageProcessorPil
+
+class AmosDecoder:
+ @classmethod
+ async def load(cls,bundle):
+  self=cls();self.root=Path(bundle).resolve();sys.path.insert(0,str(self.root/'serve'))
+  import amos.decoder as decoder
+  decoder.IMAGE_PIXELS=224*224
+  self.manifest=json.loads((self.root/'manifest.json').read_text())
+  self.codec=decoder.Codec(AutoTokenizer.from_pretrained(self.root/'model'),Qwen2VLImageProcessorPil.from_pretrained(self.root/'model',size={'shortest_edge':224*224,'longest_edge':224*224}),255)
+  self.codec.echo=self.manifest['echo']
+  self.temperature=decoder.calibration(self.root) or {};self.span=decoder.span_decoding(self.root)
+  self.models=[];self.functions={}
+  names=['vision','heads']
+  if self.manifest.get('text_layout')=='layer-sharded':names+=['embedding','finish']+[r['asset'].removesuffix('.aimodel') for r in self.manifest['layers']]
+  else:names+=['text']
+  for name in names:
+   m=await AIModel.load(self.root/(name+'.aimodel'),SpecializationOptions.default());self.models.append(m);self.functions[name]=m.load_function('main')
+  return self
+
+ async def raw(self,state,questions,images=()):
+  from amos.decoder import expand
+  from PIL import Image
+  def resize(im):return im.convert('RGB').resize((224,224),Image.Resampling.BICUBIC)
+  images=[resize(im) for im in images]
+  questions=[{**q,'options':[resize(o) if isinstance(o,Image.Image) else o for o in q['options']]} if 'options' in q else q for q in questions]
+  flat,owner=expand(questions)
+  ids,at,extra=self.codec.encode_case(state,flat,images)
+  n=len(ids);L=self.manifest['sequence_length'];d=self.manifest['hidden_size']
+  if n>L:raise ValueError(f'Request needs {n} tokens; Core AI capacity is {L}.')
+  args={'input_ids':np.full((1,L),self.codec.pad,np.int32),'attention_mask':np.zeros((1,L),np.int32),'position_ids':np.zeros((4,1,L),np.int32),'visual_embeddings':np.zeros((1,L,d),np.float32),'visual_mask':np.zeros((1,L,1),bool)}
+  args['input_ids'][0,:n]=ids;args['attention_mask'][0,:n]=1;args['position_ids'][0,0,:n]=np.arange(n)
+  image_at={s:(rows,cols) for s,rows,cols in extra.get('images',[])}
+  features=[]
+  if image_at:
+   pv=extra['pixel_values'].numpy()
+   for start in range(0,len(pv),196):
+    out=await self.functions['vision']({'pixel_values':NDArray(pv[start:start+196].astype(np.float32))});features.append(out[self.manifest['vision_output']].numpy())
+  cursor=0;offset=0;fi=0
+  while cursor<n:
+   if cursor in image_at:
+    rows,cols=image_at[cursor]
+    if (rows,cols)!=(7,7):raise ValueError('Only 224x224 image grids are supported')
+    end=cursor+49;args['position_ids'][1,0,cursor:end]=offset;args['position_ids'][2,0,cursor:end]=offset+np.repeat(np.arange(7),7);args['position_ids'][3,0,cursor:end]=offset+np.tile(np.arange(7),7)
+    args['visual_embeddings'][0,cursor:end]=features[fi];args['visual_mask'][0,cursor:end]=True;offset+=7;cursor=end;fi+=1
+   else:
+    args['position_ids'][1:,0,cursor]=offset;offset+=1;cursor+=1
+  if self.manifest.get('text_layout')=='layer-sharded':
+   result=await self.functions['embedding']({k:NDArray(v) for k,v in args.items() if k!='attention_mask'})
+   h,cos,sin=[result[k] for k in self.manifest['embedding_outputs']]
+   causal=(np.arange(L)[:,None]>=np.arange(L)[None,:])[None,None]&args['attention_mask'][:,None,None,:].astype(bool)
+   for layer in self.manifest['layers']:
+    result=await self.functions[layer['asset'].removesuffix('.aimodel')]({'hidden_states':h,'cos':cos,'sin':sin,'position_ids':NDArray(args['position_ids'][0].copy()),'attention_mask':NDArray(causal)})
+    h=result[layer['output']]
+   output=await self.functions['finish']({'hidden_states':h})
+  else:output=await self.functions['text']({k:NDArray(v) for k,v in args.items()})
+  hidden,logits=[output[k].numpy() for k in self.manifest['outputs']]
+  head_results={}
+  for i,(q,pos) in enumerate(zip(flat,at)):
+   if q['type'] not in ['span','region']:continue
+   first,offsets=extra['state'];state_hidden=np.zeros((L,d),np.float32);state_hidden[:len(offsets)]=hidden[0,first:first+len(offsets)]
+   cells=np.zeros((49,d),np.float32)
+   if extra.get('images'):
+    start,rows,cols=extra['images'][0];cells=hidden[0,start:start+49].copy()
+   if q['type']=='region' and not extra.get('images'):raise ValueError('Region requires a state image')
+   ho=await self.functions['heads']({'question_hidden':NDArray(hidden[0,pos].copy()),'state_hidden':NDArray(state_hidden),'image_hidden':NDArray(cells)})
+   span,region,box=[torch.from_numpy(ho[k].numpy().copy()) for k in self.manifest['head_outputs']]
+   head_results[i]=span[:,:len(offsets)] if q['type']=='span' else torch.cat([region,box]) if self.manifest['has_box'] else region
+  return {'flat':flat,'owner':owner,'at':at,'extra':extra,'logits':logits[0],'heads':head_results,'tokens':n,'hidden':hidden[0]}
+
+ async def predict(self,state,questions,images=()):
+  from amos.decoder import options,decode_head
+  raw=await self.raw(state,questions,images);out=[None]*len(questions)
+  for j,(q,k,pos) in enumerate(zip(raw['flat'],raw['owner'],raw['at'])):
+   typ=questions[k]['type']
+   if q['type'] in ['span','region']:
+    out[k]=decode_head(q,raw['heads'][j],raw['extra'],state,self.manifest['keep'],self.span);continue
+   logits=torch.tensor(raw['logits'][pos,:len(options(q))]);p=(logits/self.temperature.get(q['type'],1)).softmax(-1).tolist()
+   if typ=='multi':out[k]=out[k] or {'type':typ,'probs':[]};out[k]['probs'].append(p[0])
+   elif typ=='noul':out[k]={'type':typ,'p_yes':p[0]}
+   else:
+    out[k]={'type':typ,'probs':p,'choice':int(np.argmax(p))}
+    if typ=='score':out[k]['expected']=sum(i*v for i,v in enumerate(p))
+  return out
+
+ async def request(self,req):
+  from amos.serve import parse,answer
+  from PIL import Image
+  import base64,io
+  def image(s):return Image.open(io.BytesIO(base64.b64decode(s.split(',')[-1]))).convert('RGB')
+  state,ids,qs=parse(req);ims=[image(s) for s in req.get('images',[])]
+  qs=[{**q,'options':[image(o['image']) if isinstance(o,dict) else o for o in q['options']]} if 'options' in q else q for q in qs]
+  rs=await self.predict(state,qs,ims)
+  return {'answers':{i:answer(q,r) for i,q,r in zip(ids,qs,rs)}}

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