Instructions to use numind/NuExtract-2.0-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use numind/NuExtract-2.0-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="numind/NuExtract-2.0-8B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("numind/NuExtract-2.0-8B") model = AutoModelForImageTextToText.from_pretrained("numind/NuExtract-2.0-8B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use numind/NuExtract-2.0-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "numind/NuExtract-2.0-8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "numind/NuExtract-2.0-8B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/numind/NuExtract-2.0-8B
- SGLang
How to use numind/NuExtract-2.0-8B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "numind/NuExtract-2.0-8B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "numind/NuExtract-2.0-8B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "numind/NuExtract-2.0-8B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "numind/NuExtract-2.0-8B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use numind/NuExtract-2.0-8B with Docker Model Runner:
docker model run hf.co/numind/NuExtract-2.0-8B
Upload processor
Browse files- chat_template.json +1 -1
- preprocessor_config.json +3 -3
- tokenizer.json +2 -2
- tokenizer_config.json +7 -3
chat_template.json
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{
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"chat_template": "{% set image_placeholder = '<|vision_start|><|image_pad|><|vision_end|>' %}\n{% for message in messages %}\n {
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}
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{
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"chat_template": "{%- set image_placeholder = '<|vision_start|><|image_pad|><|vision_end|>' -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'user' -%}\n {%- if loop.first and message['role'] != 'system' -%}\n {{- '<|im_start|>system\n' -}}\n {%- if template -%}\n {#--- If template, extraction task ---#}\n {{- 'You are NuExtract, an information extraction tool created by NuMind.' -}}\n {%- else -%}\n {#--- Else, template generation task ---#}\n {{- 'You are a helpful assistant.' -}}\n {%- endif -%}\n {{ '<|im_end|>\n' }}\n {%- endif -%}\n {{- '<|im_start|>' + message['role'] + '\n' -}}\n {%- if template -%}\n {#--- Template Section ---#}\n {{- '# Template:\n' -}}\n {{- template -}}\n {{- '\n' -}}\n \n {%- if examples -%}\n {#--- Examples can only exist in the extraction task ---#}\n {{- '# Examples:\n' -}}\n {%- for example in examples -%}\n {{- '## Input:\n' -}}\n {%- if example['input'] is mapping and (example['input']['type'] == 'image' or example['input']['type'] == 'image_url') -%}\n {{- image_placeholder | trim -}}\n {%- elif example['input'] == '<image>' -%}\n {#--- Keep compatibility with <image> for now ---#}\n {{- image_placeholder | trim -}}\n {%- else -%}\n {#--- Text input example ---#}\n {{- example['input'] -}}\n {%- endif -%}\n {{- '\n' -}}\n {{- '## Output:\n' -}}\n {{- example['output'] -}}\n {{- '\n' -}}\n {%- endfor -%}\n {%- endif -%}\n {{- '# Context:\n' -}}\n {%- endif -%}\n \n {%- if message['content'] is string -%}\n {#--- Simple string content ---#}\n message['content'] | trim -}}\n {%- elif message['content'] is mapping and (message['content']['type'] == 'image' or message['content']['type'] == 'image_url') -%}\n {{- image_placeholder | trim -}}\n {%- else -%}\n {#--- List of content items (mixed text/images) ---#}\n {#--- First, determine what the actual input content is (not ICL images) ---#}\n {%- set ns = namespace(has_text_input=false, text_content='') -%}\n \n {#--- Count content types and identify actual input document ---#}\n {%- for content in message['content'] -%}\n {%- if content is mapping and content.get('type') == 'text' -%}\n {%- if content.get('text') != '<image>' -%}\n {#--- Keep compatibility with <image> for now ---#}\n {%- set ns.has_text_input = true -%}\n {%- set ns.text_content = content['text'] -%}\n {%- endif -%}\n {%- elif content is string -%}\n {%- if content != '<image>' -%}\n {#--- Keep compatibility with <image> for now ---#}\n {%- set ns.has_text_input = true -%}\n {%- set ns.text_content = content -%}\n {%- endif -%}\n {%- endif -%}\n {%- endfor -%}\n \n {#--- Determine what to output based on actual input type ---#}\n {%- if ns.has_text_input -%}\n {#--- Main input is text, so output the text content ---#}\n {{- ns.text_content | trim -}}\n {%- else -%}\n {#--- Main input is image or <image> placeholder ---#}\n {%- set ns2 = namespace(found_image=false) -%}\n {%- for content in message['content'] -%}\n {%- if content is mapping and (content.get('type') == 'image' or content.get('type') == 'image_url') and not ns2.found_image -%}\n {{- image_placeholder | trim -}}\n {%- set ns2.found_image = true -%}\n {%- elif content is mapping and content.get('type') == 'text' and content.get('text') == '<image>' and not ns2.found_image -%}\n {#--- Keep compatibility with <image> for now ---#}\n {{- image_placeholder | trim -}}\n {%- set ns2.found_image = true -%}\n {%- elif content is string and content == '<image>' and not ns2.found_image -%}\n {#--- Keep compatibility with <image> for now ---#}\n {{- image_placeholder | trim -}}\n {%- set ns2.found_image = true -%}\n {%- endif -%}\n {%- endfor -%}\n {%- endif -%}\n {%- endif -%}\n {{- '<|im_end|>\n'}}\n {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n {{- '<|im_start|>assistant\n' -}}\n{%- endif -%}"
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}
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preprocessor_config.json
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0.26130258,
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0.27577711
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],
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"max_pixels":
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"merge_size": 2,
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"min_pixels": 200704,
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"patch_size": 14,
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"longest_edge":
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"shortest_edge":
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},
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"temporal_patch_size": 2
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}
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0.26130258,
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0.27577711
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],
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"max_pixels": 23000000,
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"merge_size": 2,
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"min_pixels": 200704,
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"patch_size": 14,
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"longest_edge": 23000000,
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"shortest_edge": 200704
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},
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"temporal_patch_size": 2
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:ba0c439f7be467bf47d12a7e6f9adc6116201056fc60c67f431c679b7c16afc8
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size 11422064
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tokenizer_config.json
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{
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"add_bos_token": false,
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"<|video_pad|>"
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],
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"bos_token": null,
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"chat_template": "{% set image_placeholder = '<|vision_start|><|image_pad|><|vision_end|>' %}\n{% for message in messages %}\n {
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": {},
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"
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"min_pixels": 200704,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"
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"processor_class": "Qwen2_5_VLProcessor",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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{
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"_commit_hash": null,
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"add_bos_token": false,
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"<|video_pad|>"
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],
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"bos_token": null,
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"chat_template": "{%- set image_placeholder = '<|vision_start|><|image_pad|><|vision_end|>' -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'user' -%}\n {%- if loop.first and message['role'] != 'system' -%}\n {{- '<|im_start|>system\n' -}}\n {%- if template -%}\n {#--- If template, extraction task ---#}\n {{- 'You are NuExtract, an information extraction tool created by NuMind.' -}}\n {%- else -%}\n {#--- Else, template generation task ---#}\n {{- 'You are a helpful assistant.' -}}\n {%- endif -%}\n {{ '<|im_end|>\n' }}\n {%- endif -%}\n {{- '<|im_start|>' + message['role'] + '\n' -}}\n {%- if template -%}\n {#--- Template Section ---#}\n {{- '# Template:\n' -}}\n {{- template -}}\n {{- '\n' -}}\n \n {%- if examples -%}\n {#--- Examples can only exist in the extraction task ---#}\n {{- '# Examples:\n' -}}\n {%- for example in examples -%}\n {{- '## Input:\n' -}}\n {%- if example['input'] is mapping and (example['input']['type'] == 'image' or example['input']['type'] == 'image_url') -%}\n {{- image_placeholder | trim -}}\n {%- elif example['input'] == '<image>' -%}\n {#--- Keep compatibility with <image> for now ---#}\n {{- image_placeholder | trim -}}\n {%- else -%}\n {#--- Text input example ---#}\n {{- example['input'] -}}\n {%- endif -%}\n {{- '\n' -}}\n {{- '## Output:\n' -}}\n {{- example['output'] -}}\n {{- '\n' -}}\n {%- endfor -%}\n {%- endif -%}\n {{- '# Context:\n' -}}\n {%- endif -%}\n \n {%- if message['content'] is string -%}\n {#--- Simple string content ---#}\n message['content'] | trim -}}\n {%- elif message['content'] is mapping and (message['content']['type'] == 'image' or message['content']['type'] == 'image_url') -%}\n {{- image_placeholder | trim -}}\n {%- else -%}\n {#--- List of content items (mixed text/images) ---#}\n {#--- First, determine what the actual input content is (not ICL images) ---#}\n {%- set ns = namespace(has_text_input=false, text_content='') -%}\n \n {#--- Count content types and identify actual input document ---#}\n {%- for content in message['content'] -%}\n {%- if content is mapping and content.get('type') == 'text' -%}\n {%- if content.get('text') != '<image>' -%}\n {#--- Keep compatibility with <image> for now ---#}\n {%- set ns.has_text_input = true -%}\n {%- set ns.text_content = content['text'] -%}\n {%- endif -%}\n {%- elif content is string -%}\n {%- if content != '<image>' -%}\n {#--- Keep compatibility with <image> for now ---#}\n {%- set ns.has_text_input = true -%}\n {%- set ns.text_content = content -%}\n {%- endif -%}\n {%- endif -%}\n {%- endfor -%}\n \n {#--- Determine what to output based on actual input type ---#}\n {%- if ns.has_text_input -%}\n {#--- Main input is text, so output the text content ---#}\n {{- ns.text_content | trim -}}\n {%- else -%}\n {#--- Main input is image or <image> placeholder ---#}\n {%- set ns2 = namespace(found_image=false) -%}\n {%- for content in message['content'] -%}\n {%- if content is mapping and (content.get('type') == 'image' or content.get('type') == 'image_url') and not ns2.found_image -%}\n {{- image_placeholder | trim -}}\n {%- set ns2.found_image = true -%}\n {%- elif content is mapping and content.get('type') == 'text' and content.get('text') == '<image>' and not ns2.found_image -%}\n {#--- Keep compatibility with <image> for now ---#}\n {{- image_placeholder | trim -}}\n {%- set ns2.found_image = true -%}\n {%- elif content is string and content == '<image>' and not ns2.found_image -%}\n {#--- Keep compatibility with <image> for now ---#}\n {{- image_placeholder | trim -}}\n {%- set ns2.found_image = true -%}\n {%- endif -%}\n {%- endfor -%}\n {%- endif -%}\n {%- endif -%}\n {{- '<|im_end|>\n'}}\n {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n {{- '<|im_start|>assistant\n' -}}\n{%- endif -%}",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": {},
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"max_length": null,
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"max_pixels": 23000000,
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"min_pixels": 200704,
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"model_max_length": 131072,
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"pad_to_multiple_of": null,
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"pad_token": "<|endoftext|>",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"processor_class": "Qwen2_5_VLProcessor",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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