Docling/docling/datamodel/vlm_model_specs.py
Peter W. J. Staar cfdf4cea25
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>
2025-06-02 17:01:06 +02:00

145 lines
4.6 KiB
Python

import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_vlm_model import (
ApiVlmOptions,
InferenceFramework,
InlineVlmOptions,
ResponseFormat,
TransformersModelType,
)
_log = logging.getLogger(__name__)
# SmolDocling
SMOLDOCLING_MLX = InlineVlmOptions(
repo_id="ds4sd/SmolDocling-256M-preview-mlx-bf16",
prompt="Convert this page to docling.",
response_format=ResponseFormat.DOCTAGS,
inference_framework=InferenceFramework.MLX,
supported_devices=[AcceleratorDevice.MPS],
scale=2.0,
temperature=0.0,
)
SMOLDOCLING_TRANSFORMERS = InlineVlmOptions(
repo_id="ds4sd/SmolDocling-256M-preview",
prompt="Convert this page to docling.",
response_format=ResponseFormat.DOCTAGS,
inference_framework=InferenceFramework.TRANSFORMERS,
transformers_model_type=TransformersModelType.AUTOMODEL_VISION2SEQ,
supported_devices=[
AcceleratorDevice.CPU,
AcceleratorDevice.CUDA,
AcceleratorDevice.MPS,
],
scale=2.0,
temperature=0.0,
)
# GraniteVision
GRANITE_VISION_TRANSFORMERS = InlineVlmOptions(
repo_id="ibm-granite/granite-vision-3.2-2b",
prompt="Convert this page to markdown. Do not miss any text and only output the bare markdown!",
response_format=ResponseFormat.MARKDOWN,
inference_framework=InferenceFramework.TRANSFORMERS,
transformers_model_type=TransformersModelType.AUTOMODEL_VISION2SEQ,
supported_devices=[
AcceleratorDevice.CPU,
AcceleratorDevice.CUDA,
AcceleratorDevice.MPS,
],
scale=2.0,
temperature=0.0,
)
GRANITE_VISION_OLLAMA = ApiVlmOptions(
url=AnyUrl("http://localhost:11434/v1/chat/completions"),
params={"model": "granite3.2-vision:2b"},
prompt="Convert this page to markdown. Do not miss any text and only output the bare markdown!",
scale=1.0,
timeout=120,
response_format=ResponseFormat.MARKDOWN,
temperature=0.0,
)
# Pixtral
PIXTRAL_12B_TRANSFORMERS = InlineVlmOptions(
repo_id="mistral-community/pixtral-12b",
prompt="Convert this page to markdown. Do not miss any text and only output the bare markdown!",
response_format=ResponseFormat.MARKDOWN,
inference_framework=InferenceFramework.TRANSFORMERS,
transformers_model_type=TransformersModelType.AUTOMODEL_VISION2SEQ,
supported_devices=[AcceleratorDevice.CPU, AcceleratorDevice.CUDA],
scale=2.0,
temperature=0.0,
)
PIXTRAL_12B_MLX = InlineVlmOptions(
repo_id="mlx-community/pixtral-12b-bf16",
prompt="Convert this page to markdown. Do not miss any text and only output the bare markdown!",
response_format=ResponseFormat.MARKDOWN,
inference_framework=InferenceFramework.MLX,
supported_devices=[AcceleratorDevice.MPS],
scale=2.0,
temperature=0.0,
)
# Phi4
PHI4_TRANSFORMERS = InlineVlmOptions(
repo_id="microsoft/Phi-4-multimodal-instruct",
prompt="Convert this page to MarkDown. Do not miss any text and only output the bare markdown",
trust_remote_code=True,
response_format=ResponseFormat.MARKDOWN,
inference_framework=InferenceFramework.TRANSFORMERS,
transformers_model_type=TransformersModelType.AUTOMODEL_CAUSALLM,
supported_devices=[AcceleratorDevice.CPU, AcceleratorDevice.CUDA],
scale=2.0,
temperature=0.0,
extra_generation_config=dict(num_logits_to_keep=0),
)
# Qwen
QWEN25_VL_3B_MLX = InlineVlmOptions(
repo_id="mlx-community/Qwen2.5-VL-3B-Instruct-bf16",
prompt="Convert this page to markdown. Do not miss any text and only output the bare markdown!",
response_format=ResponseFormat.MARKDOWN,
inference_framework=InferenceFramework.MLX,
supported_devices=[AcceleratorDevice.MPS],
scale=2.0,
temperature=0.0,
)
# Gemma-3
GEMMA3_12B_MLX = InlineVlmOptions(
repo_id="mlx-community/gemma-3-12b-it-bf16",
prompt="Convert this page to markdown. Do not miss any text and only output the bare markdown!",
response_format=ResponseFormat.MARKDOWN,
inference_framework=InferenceFramework.MLX,
supported_devices=[AcceleratorDevice.MPS],
scale=2.0,
temperature=0.0,
)
GEMMA3_27B_MLX = InlineVlmOptions(
repo_id="mlx-community/gemma-3-27b-it-bf16",
prompt="Convert this page to markdown. Do not miss any text and only output the bare markdown!",
response_format=ResponseFormat.MARKDOWN,
inference_framework=InferenceFramework.MLX,
supported_devices=[AcceleratorDevice.MPS],
scale=2.0,
temperature=0.0,
)
class VlmModelType(str, Enum):
SMOLDOCLING = "smoldocling"
GRANITE_VISION = "granite_vision"
GRANITE_VISION_OLLAMA = "granite_vision_ollama"