Image-Text-to-Text
MLX
Safetensors
multilingual
internvl_chat
vision-language
ocr
document-intelligence
qianfan
apple-silicon
custom_code
Eval Results
4-bit precision
Instructions to use jason1966/Qianfan-OCR-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jason1966/Qianfan-OCR-MLX-4bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("jason1966/Qianfan-OCR-MLX-4bit") config = load_config("jason1966/Qianfan-OCR-MLX-4bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| # -------------------------------------------------------- | |
| # InternVL | |
| # Copyright (c) 2024 OpenGVLab | |
| # Licensed under The MIT License [see NOTICE for details] | |
| # -------------------------------------------------------- | |
| import copy | |
| from typing import Dict, Any, Optional | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| from .configuration_intern_vit import InternVisionConfig | |
| logger = logging.get_logger(__name__) | |
| class InternVLChatConfig(PretrainedConfig): | |
| model_type = 'internvl_chat' | |
| is_composition = True | |
| def __init__( | |
| self, | |
| vision_config: Optional[Dict[str, Any]] = None, | |
| llm_config: Optional[Dict[str, Any]] = None, | |
| use_backbone_lora=0, | |
| use_llm_lora=0, | |
| select_layer=-1, | |
| force_image_size=None, | |
| downsample_ratio=0.5, | |
| template=None, | |
| dynamic_image_size=False, | |
| use_thumbnail=False, | |
| ps_version="v1", | |
| min_dynamic_patch=1, | |
| max_dynamic_patch=6, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| if vision_config is None: | |
| vision_config = {'architectures': ['InternVisionModel']} | |
| logger.info('vision_config is None. Initializing the InternVisionConfig with default values.') | |
| if llm_config is None: | |
| llm_config = {'architectures': ['Qwen2ForCausalLM']} | |
| logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).') | |
| assert 'architectures' in llm_config, "Should specify architecture in llm_config" | |
| if isinstance(vision_config, dict): | |
| self.vision_config = InternVisionConfig(**vision_config) | |
| else: | |
| self.vision_config = vision_config | |
| if isinstance(llm_config, dict): | |
| architecture: str = llm_config['architectures'][0] | |
| if architecture == 'LlamaForCausalLM': | |
| from transformers import LlamaConfig | |
| self.llm_config = LlamaConfig(**llm_config) | |
| elif architecture == 'Qwen2ForCausalLM': | |
| from transformers import Qwen2Config | |
| self.llm_config = Qwen2Config(**llm_config) | |
| elif architecture == 'Qwen3MoeForCausalLM': | |
| from transformers import Qwen3MoeConfig | |
| self.llm_config = Qwen3MoeConfig(**llm_config) | |
| elif architecture == 'Qwen3ForCausalLM': | |
| from transformers import Qwen3Config | |
| self.llm_config = Qwen3Config(**llm_config) | |
| else: | |
| raise ValueError('Unsupported architecture: {}'.format(architecture)) | |
| else: | |
| self.llm_config = llm_config | |
| self.use_backbone_lora = use_backbone_lora | |
| self.use_llm_lora = use_llm_lora | |
| self.select_layer = select_layer | |
| self.force_image_size = force_image_size | |
| self.downsample_ratio = downsample_ratio | |
| self.template = template | |
| self.dynamic_image_size = dynamic_image_size | |
| self.use_thumbnail = use_thumbnail | |
| self.ps_version = ps_version # pixel shuffle version | |
| self.min_dynamic_patch = min_dynamic_patch | |
| self.max_dynamic_patch = max_dynamic_patch | |
| self.tie_word_embeddings = self.llm_config.tie_word_embeddings | |
| logger.info(f'vision_select_layer: {self.select_layer}') | |
| logger.info(f'ps_version: {self.ps_version}') | |
| logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}') | |
| logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}') | |
| def to_dict(self): | |
| """ | |
| Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. | |
| Returns: | |
| `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, | |
| """ | |
| output = copy.deepcopy(self.__dict__) | |
| output['vision_config'] = self.vision_config.to_dict() | |
| output['llm_config'] = self.llm_config.to_dict() | |
| output['model_type'] = self.__class__.model_type | |
| output['use_backbone_lora'] = self.use_backbone_lora | |
| output['use_llm_lora'] = self.use_llm_lora | |
| output['select_layer'] = self.select_layer | |
| output['force_image_size'] = self.force_image_size | |
| output['downsample_ratio'] = self.downsample_ratio | |
| output['template'] = self.template | |
| output['dynamic_image_size'] = self.dynamic_image_size | |
| output['use_thumbnail'] = self.use_thumbnail | |
| output['ps_version'] = self.ps_version | |
| output['min_dynamic_patch'] = self.min_dynamic_patch | |
| output['max_dynamic_patch'] = self.max_dynamic_patch | |
| return output | |