ZacharyZhang-NY
0ee99e271d
Real-models implementation: multimodal ingestion, Context Cartridge memory, sparse + Qwen embedding retrieval, Cache Fabric, Qwen runtime adapters, verifier contracts, Visual HashHop proofs, BF16 LoRA fine-tuning, and a local dashboard. Verified by a 15-check PRD proof suite on the live model.
480 lines
13 KiB
Python
480 lines
13 KiB
Python
from __future__ import annotations
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from datetime import datetime, timezone
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from enum import StrEnum
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from pathlib import Path
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from typing import Any, Literal
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from pydantic import BaseModel, Field
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def utc_now() -> datetime:
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return datetime.now(timezone.utc)
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class RuntimeProfile(StrEnum):
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live_demo = "live_demo"
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extreme_context = "extreme_context"
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library_100m = "library_100m"
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research_theater = "research_theater"
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class TrainingAdapterKind(StrEnum):
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hme_core_lora = "hme_core_lora"
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hme_vision_lora = "hme_vision_lora"
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hme_ttt_vl = "hme_ttt_vl"
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hme_visual_mtp = "hme_visual_mtp"
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hme_router = "hme_router"
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hme_retrieval = "hme_retrieval"
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class Modality(StrEnum):
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text = "text"
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code = "code"
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image = "image"
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pdf_page = "pdf_page"
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pdf_region = "pdf_region"
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video_frame = "video_frame"
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ui_screenshot = "ui_screenshot"
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mixed = "mixed"
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class LayoutBox(BaseModel):
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x: float
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y: float
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w: float
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h: float
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page: int | None = None
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frame_second: float | None = None
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class TrustScores(BaseModel):
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parser: float = Field(ge=0.0, le=1.0)
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ocr: float = Field(default=0.0, ge=0.0, le=1.0)
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model: float = Field(default=0.0, ge=0.0, le=1.0)
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class EvidenceAtom(BaseModel):
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atom_id: str
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modality: Modality
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source_id: str
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source: str
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time: datetime = Field(default_factory=utc_now)
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text: str = ""
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image_ref: str | None = None
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embedding_refs: list[str] = Field(default_factory=list)
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layout: LayoutBox | None = None
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relations: list[str] = Field(default_factory=list)
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trust: TrustScores
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token_equivalent: int = Field(default=0, ge=0)
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metadata: dict[str, Any] = Field(default_factory=dict)
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class SourceRecord(BaseModel):
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source_id: str
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path: str
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kind: str
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mime: str
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sha256: str
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size_bytes: int
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created_at: datetime = Field(default_factory=utc_now)
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metadata: dict[str, Any] = Field(default_factory=dict)
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class IngestResponse(BaseModel):
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source_count: int
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atom_count: int
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cartridge_id: str | None
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token_equivalent: int
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sources: list[SourceRecord]
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class RetrievalHit(BaseModel):
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atom: EvidenceAtom
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sparse_score: float
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dense_score: float = 0.0
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graph_score: float = 0.0
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final_score: float
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class ContextPlan(BaseModel):
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question: str
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profile: RuntimeProfile
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required_modalities: list[Modality]
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evidence_budget_atoms: int
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token_budget: int
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retrieval_query: str
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compression_strategy: str
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verifier_contract: list[str]
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class CompiledContext(BaseModel):
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plan: ContextPlan
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hits: list[RetrievalHit]
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packed_text: str
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token_equivalent: int
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cache_trace_id: str
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class CitationReport(BaseModel):
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cited_atom_ids: list[str]
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missing_atom_ids: list[str]
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citation_accuracy: float
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contradiction_notes: list[str]
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confidence: float
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class AnswerResponse(BaseModel):
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answer: str
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citations: list[str]
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context: CompiledContext
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verifier: CitationReport
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runtime_backend: str
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class CacheLayerStatus(BaseModel):
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layer: str
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entries: int
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bytes_estimate: int
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hit_count: int
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miss_count: int
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last_event_at: datetime | None
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class MemoryStatus(BaseModel):
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active_atoms: int
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active_token_equivalent: int
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cartridges: int
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library_token_equivalent: int
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layers: list[CacheLayerStatus]
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class RuntimeStatus(BaseModel):
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backend: str
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model_id: str
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available: bool
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detail: str
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adapter_path: str | None = None
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adapter_kind: TrainingAdapterKind | None = None
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adapter_id: str | None = None
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adapter_sha256: str | None = None
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gpu_name: str | None = None
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cuda: str | None = None
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vram_total_mb: int | None = None
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vram_used_mb: int | None = None
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class SystemStatus(BaseModel):
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app: str = "Ely-Eye"
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version: str
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runtime: RuntimeStatus
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memory: MemoryStatus
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profiles: dict[str, dict[str, Any]]
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data_home: str
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class CartridgeManifest(BaseModel):
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cartridge_id: str
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name: str
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created_at: datetime
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model_base: str
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source_count: int
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atom_count: int
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token_equivalent: int
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artifacts: dict[str, str]
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dna: str | None = None
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class AdapterManifest(BaseModel):
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adapter_id: str
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kind: TrainingAdapterKind
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created_at: datetime
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proof_version: int = 1
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training_method: str = "BF16 LoRA"
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precision: str = "bf16"
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framework: str = "unsloth_patched_transformers_peft"
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base_model: str
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dataset_path: str
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dataset_sha256: str
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sample_count: int
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max_steps: int
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max_length: int
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rank: int
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alpha: int
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target_modules: list[str]
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trainable_params: int
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total_params: int
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optimizer: str
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optimizer_family: str | None = None
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learning_rate: float
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gradient_accumulation_steps: int
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gradient_checkpointing: bool = False
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gradient_checkpointing_mode: str | None = None
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autocast_dtype: str | None = None
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unsloth_version: str | None = None
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triton_version: str | None = None
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xformers_version: str | None = None
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unsloth_model_type: str | None = None
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bf16: bool
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final_loss: float | None
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loss_history: list[float] = Field(default_factory=list)
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initial_trainable_sha256: str | None = None
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final_trainable_sha256: str | None = None
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initial_trainable_l2: float | None = None
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final_trainable_l2: float | None = None
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optimizer_update_count: int = 0
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max_optimizer_update_l2: float | None = None
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loss_delta: float | None = None
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training_proof_path: str | None = None
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training_proof_sha256: str | None = None
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training_trace_path: str | None = None
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training_trace_sha256: str | None = None
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training_summary_path: str | None = None
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training_summary_sha256: str | None = None
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training_started_at: datetime | None = None
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training_finished_at: datetime | None = None
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training_wall_seconds: float | None = None
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training_device: str | None = None
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torch_version: str | None = None
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cuda_version: str | None = None
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adapter_weight_sha256: str | None = None
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adapter_tensor_count: int = 0
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adapter_lora_tensor_count: int = 0
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adapter_total_elements: int = 0
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adapter_nonzero_elements: int = 0
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adapter_max_abs: float | None = None
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adapter_weights_finite: bool = False
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artifacts: dict[str, str]
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sha256: str
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cartridge_id: str | None = None
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class AdapterStatus(BaseModel):
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name: str
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kind: TrainingAdapterKind
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proof_version: int = 1
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training_method: str = "BF16 LoRA"
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precision: str = "bf16"
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framework: str = "unsloth_patched_transformers_peft"
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base_model: str
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path: str
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adapter_id: str
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sample_count: int
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max_steps: int
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trainable_params: int
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final_loss: float | None
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sha256: str
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weight_bytes: int
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safetensor_keys: int
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cartridge_id: str | None = None
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cartridge_bound: bool
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training_trace_path: str | None = None
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training_trace_sha256: str | None = None
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training_trace_steps: int = 0
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training_proof_path: str | None = None
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training_proof_sha256: str | None = None
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optimizer: str | None = None
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optimizer_family: str | None = None
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gradient_checkpointing: bool = False
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gradient_checkpointing_mode: str | None = None
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autocast_dtype: str | None = None
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unsloth_version: str | None = None
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triton_version: str | None = None
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xformers_version: str | None = None
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unsloth_model_type: str | None = None
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optimizer_update_count: int = 0
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max_optimizer_update_l2: float | None = None
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loss_delta: float | None = None
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training_summary_path: str | None = None
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training_wall_seconds: float | None = None
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training_device: str | None = None
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bf16: bool = False
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torch_version: str | None = None
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cuda_version: str | None = None
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adapter_weight_sha256: str | None = None
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adapter_tensor_count: int = 0
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adapter_lora_tensor_count: int = 0
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adapter_total_elements: int = 0
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adapter_nonzero_elements: int = 0
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adapter_max_abs: float | None = None
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adapter_weights_finite: bool = False
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class CartridgeAssetStatus(BaseModel):
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name: str
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path: str
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sha256: str
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size_bytes: int
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item_count: int
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dtype: str
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status: Literal["ready", "missing"]
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class CartridgeAssetReport(BaseModel):
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cartridge_id: str
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status: Literal["ready", "incomplete"]
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created_at: datetime = Field(default_factory=utc_now)
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assets: list[CartridgeAssetStatus]
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class BenchmarkSpec(BaseModel):
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name: str
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target: str
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dataset: str
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task_type: str
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runner: str
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metric: str
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class BenchmarkRegistryReport(BaseModel):
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status: Literal["ready", "incomplete"]
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created_at: datetime = Field(default_factory=utc_now)
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benchmarks: list[BenchmarkSpec]
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class BenchmarkSourceStatus(BaseModel):
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name: str
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dataset: str
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status: Literal["ready", "unavailable"]
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sha: str | None = None
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gated: bool | str | None = None
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private: bool | None = None
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sibling_count: int = 0
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error: str | None = None
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class BenchmarkSourceReport(BaseModel):
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status: Literal["ready", "incomplete"]
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created_at: datetime = Field(default_factory=utc_now)
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sources: list[BenchmarkSourceStatus]
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class BenchmarkSampleStatus(BaseModel):
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name: str
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dataset: str
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source_dataset: str | None = None
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source_sha: str | None = None
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source_note: str | None = None
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status: Literal["loaded", "gated", "failed"]
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config: str | None = None
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split: str | None = None
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sample_id: str | None = None
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fields: list[str] = Field(default_factory=list)
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question_preview: str | None = None
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answer_preview: str | None = None
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sample_sha256: str | None = None
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media_artifacts: list[str] = Field(default_factory=list)
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error: str | None = None
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class BenchmarkSampleReport(BaseModel):
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status: Literal["ready", "partial", "failed"]
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created_at: datetime = Field(default_factory=utc_now)
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samples: list[BenchmarkSampleStatus]
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class BenchmarkPredictionStatus(BaseModel):
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name: str
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dataset: str
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status: Literal["predicted", "gated", "failed"]
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config: str | None = None
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split: str | None = None
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sample_id: str | None = None
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task_type: str
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metric: str
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prompt_sha256: str | None = None
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sample_sha256: str | None = None
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prediction: str | None = None
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answer: str | None = None
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score: float | None = None
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runtime_backend: str | None = None
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adapter_id: str | None = None
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adapter_sha256: str | None = None
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input_tokens: int | None = None
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output_tokens: int | None = None
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media_artifacts: list[str] = Field(default_factory=list)
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error: str | None = None
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class BenchmarkPredictionReport(BaseModel):
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status: Literal["ready", "partial", "failed"]
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created_at: datetime = Field(default_factory=utc_now)
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predictions: list[BenchmarkPredictionStatus]
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class RuntimeLaunchProfile(BaseModel):
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name: str
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runtime: str
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role: str
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context_tokens: int
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script: str
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command: list[str]
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environment: dict[str, str] = Field(default_factory=dict)
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required_features: list[str] = Field(default_factory=list)
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class RuntimeProfileValidation(BaseModel):
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name: str
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runtime: str
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status: Literal["ready", "missing"]
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script_exists: bool
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package: str | None = None
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package_version: str | None = None
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executable: str | None = None
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wsl_gpu: bool = False
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probe_command: list[str] = Field(default_factory=list)
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probe_status: Literal["passed", "failed", "skipped"] = "skipped"
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probe_exit_code: int | None = None
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probe_output: str | None = None
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detail: str
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class RuntimeProfileReport(BaseModel):
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status: Literal["ready", "incomplete"]
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created_at: datetime = Field(default_factory=utc_now)
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profiles: list[RuntimeLaunchProfile]
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validations: list[RuntimeProfileValidation] = Field(default_factory=list)
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class HashHopProof(BaseModel):
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proof_id: str
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kind: Literal["text_hashhop", "visual_hashhop"]
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hops: int
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token_equivalent: int
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query_id: str
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expected_target_id: str
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model_target_id: str | None = None
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passed: bool | None = None
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artifacts: list[str]
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created_at: datetime = Field(default_factory=utc_now)
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class ProofCheck(BaseModel):
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name: str
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requirement: str
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status: Literal["passed", "failed"]
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evidence: list[str]
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detail: str
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class ProofSuiteReport(BaseModel):
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proof_id: str
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cartridge_id: str
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status: Literal["passed", "failed"]
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created_at: datetime = Field(default_factory=utc_now)
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checks: list[ProofCheck]
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artifacts: dict[str, str]
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dna_before: str | None = None
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dna_after: str | None = None
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class UploadIngestRequest(BaseModel):
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cartridge_name: str | None = None
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dense_embeddings: bool | None = None
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class PathIngestRequest(BaseModel):
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path: Path
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cartridge_name: str | None = None
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dense_embeddings: bool | None = None
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