feat: new vlm-models support (#1570)
* feat: adding new vlm-models support Signed-off-by: Peter Staar <taa@zurich.ibm.com> * fixed the transformers Signed-off-by: Peter Staar <taa@zurich.ibm.com> * got microsoft/Phi-4-multimodal-instruct to work Signed-off-by: Peter Staar <taa@zurich.ibm.com> * working on vlm's Signed-off-by: Peter Staar <taa@zurich.ibm.com> * refactoring the VLM part Signed-off-by: Peter Staar <taa@zurich.ibm.com> * all working, now serious refacgtoring necessary Signed-off-by: Peter Staar <taa@zurich.ibm.com> * refactoring the download_model Signed-off-by: Peter Staar <taa@zurich.ibm.com> * added the formulate_prompt Signed-off-by: Peter Staar <taa@zurich.ibm.com> * pixtral 12b runs via MLX and native transformers Signed-off-by: Peter Staar <taa@zurich.ibm.com> * added the VlmPredictionToken Signed-off-by: Peter Staar <taa@zurich.ibm.com> * refactoring minimal_vlm_pipeline Signed-off-by: Peter Staar <taa@zurich.ibm.com> * fixed the MyPy Signed-off-by: Peter Staar <taa@zurich.ibm.com> * added pipeline_model_specializations file Signed-off-by: Peter Staar <taa@zurich.ibm.com> * need to get Phi4 working again ... Signed-off-by: Peter Staar <taa@zurich.ibm.com> * finalising last points for vlms support Signed-off-by: Peter Staar <taa@zurich.ibm.com> * fixed the pipeline for Phi4 Signed-off-by: Peter Staar <taa@zurich.ibm.com> * streamlining all code Signed-off-by: Peter Staar <taa@zurich.ibm.com> * reformatted the code Signed-off-by: Peter Staar <taa@zurich.ibm.com> * fixing the tests Signed-off-by: Peter Staar <taa@zurich.ibm.com> * added the html backend to the VLM pipeline Signed-off-by: Peter Staar <taa@zurich.ibm.com> * fixed the static load_from_doctags Signed-off-by: Peter Staar <taa@zurich.ibm.com> * restore stable imports Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * use AutoModelForVision2Seq for Pixtral and review example (including rename) Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * remove unused value Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * refactor instances of VLM models Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * skip compare example in CI Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * use lowercase and uppercase only Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * add new minimal_vlm example and refactor pipeline_options_vlm_model for cleaner import Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * rename pipeline_vlm_model_spec Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * move more argument to options and simplify model init Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * add supported_devices Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * remove not-needed function Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * exclude minimal_vlm Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * missing file Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * add message for transformers version Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * rename to specs Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * use module import and remove MLX from non-darwin Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * remove hf_vlm_model and add extra_generation_args Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * use single HF VLM model class Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * remove torch type Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * add docs for vision models Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> --------- Signed-off-by: Peter Staar <taa@zurich.ibm.com> Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> Co-authored-by: Michele Dolfi <dol@zurich.ibm.com>
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docling/datamodel/pipeline_options_vlm_model.py
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docling/datamodel/pipeline_options_vlm_model.py
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from enum import Enum
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from typing import Any, Dict, List, Literal
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from pydantic import AnyUrl, BaseModel
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from typing_extensions import deprecated
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from docling.datamodel.accelerator_options import AcceleratorDevice
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class BaseVlmOptions(BaseModel):
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kind: str
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prompt: str
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class ResponseFormat(str, Enum):
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DOCTAGS = "doctags"
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MARKDOWN = "markdown"
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HTML = "html"
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class InferenceFramework(str, Enum):
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MLX = "mlx"
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TRANSFORMERS = "transformers"
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class TransformersModelType(str, Enum):
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AUTOMODEL = "automodel"
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AUTOMODEL_VISION2SEQ = "automodel-vision2seq"
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AUTOMODEL_CAUSALLM = "automodel-causallm"
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class InlineVlmOptions(BaseVlmOptions):
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kind: Literal["inline_model_options"] = "inline_model_options"
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repo_id: str
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trust_remote_code: bool = False
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load_in_8bit: bool = True
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llm_int8_threshold: float = 6.0
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quantized: bool = False
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inference_framework: InferenceFramework
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transformers_model_type: TransformersModelType = TransformersModelType.AUTOMODEL
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response_format: ResponseFormat
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supported_devices: List[AcceleratorDevice] = [
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AcceleratorDevice.CPU,
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AcceleratorDevice.CUDA,
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AcceleratorDevice.MPS,
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]
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scale: float = 2.0
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temperature: float = 0.0
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stop_strings: List[str] = []
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extra_generation_config: Dict[str, Any] = {}
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use_kv_cache: bool = True
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max_new_tokens: int = 4096
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@property
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def repo_cache_folder(self) -> str:
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return self.repo_id.replace("/", "--")
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@deprecated("Use InlineVlmOptions instead.")
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class HuggingFaceVlmOptions(InlineVlmOptions):
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pass
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class ApiVlmOptions(BaseVlmOptions):
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kind: Literal["api_model_options"] = "api_model_options"
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url: AnyUrl = AnyUrl(
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"http://localhost:11434/v1/chat/completions"
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) # Default to ollama
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headers: Dict[str, str] = {}
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params: Dict[str, Any] = {}
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scale: float = 2.0
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timeout: float = 60
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concurrency: int = 1
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response_format: ResponseFormat
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