Image-Text-to-Text
MLX
Core ML
Safetensors
Transformers
llava_qwen2
text-generation
fastvlm
mlx-vlm
conversational
custom_code
vision-language
multimodal
apple-silicon
quantized
7B
q4 / 4-bit quantized
4-bit precision
4-bit precision
q4
Instructions to use dbaek111/fastvlm-7b-mlx-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use dbaek111/fastvlm-7b-mlx-q4 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("dbaek111/fastvlm-7b-mlx-q4") config = load_config("dbaek111/fastvlm-7b-mlx-q4") # 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) - Transformers
How to use dbaek111/fastvlm-7b-mlx-q4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="dbaek111/fastvlm-7b-mlx-q4", trust_remote_code=True) 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 AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("dbaek111/fastvlm-7b-mlx-q4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use dbaek111/fastvlm-7b-mlx-q4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dbaek111/fastvlm-7b-mlx-q4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dbaek111/fastvlm-7b-mlx-q4", "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/dbaek111/fastvlm-7b-mlx-q4
- SGLang
How to use dbaek111/fastvlm-7b-mlx-q4 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 "dbaek111/fastvlm-7b-mlx-q4" \ --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": "dbaek111/fastvlm-7b-mlx-q4", "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 "dbaek111/fastvlm-7b-mlx-q4" \ --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": "dbaek111/fastvlm-7b-mlx-q4", "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" } } ] } ] }' - Pi
How to use dbaek111/fastvlm-7b-mlx-q4 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "dbaek111/fastvlm-7b-mlx-q4"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "dbaek111/fastvlm-7b-mlx-q4" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use dbaek111/fastvlm-7b-mlx-q4 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "dbaek111/fastvlm-7b-mlx-q4"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default dbaek111/fastvlm-7b-mlx-q4
Run Hermes
hermes
- OpenClaw new
How to use dbaek111/fastvlm-7b-mlx-q4 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "dbaek111/fastvlm-7b-mlx-q4"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "dbaek111/fastvlm-7b-mlx-q4" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use dbaek111/fastvlm-7b-mlx-q4 with Docker Model Runner:
docker model run hf.co/dbaek111/fastvlm-7b-mlx-q4
Add tokenizer, processor, generation config, and licenses
Browse files- LICENSE +49 -0
- LICENSE_MODEL +88 -0
- added_tokens.json +24 -0
- generation_config.json +6 -0
- merges.txt +0 -0
- preprocessor_config.json +28 -0
- special_tokens_map.json +31 -0
- vocab.json +0 -0
LICENSE
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Copyright (C) 2025 Apple Inc. All Rights Reserved.
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IMPORTANT: This Apple software is supplied to you by Apple
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Inc. ("Apple") in consideration of your agreement to the following
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redistribute this Apple software.
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In consideration of your agreement to abide by the following terms, and
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subject to these terms, Apple grants you a personal, non-exclusive
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Neither the name, trademarks, service marks or logos of Apple Inc. may
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The Apple Software is provided by Apple on an "AS IS" basis. APPLE
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MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION
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THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY AND FITNESS
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FOR A PARTICULAR PURPOSE, REGARDING THE APPLE SOFTWARE OR ITS USE AND
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OPERATION ALONE OR IN COMBINATION WITH YOUR PRODUCTS.
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IN NO EVENT SHALL APPLE BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL
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OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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INTERRUPTION) ARISING IN ANY WAY OUT OF THE USE, REPRODUCTION,
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STRICT LIABILITY OR OTHERWISE, EVEN IF APPLE HAS BEEN ADVISED OF THE
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-------------------------------------------------------------------------------
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SOFTWARE DISTRIBUTED WITH ML-FASTVLM:
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The ml-fastvlm software includes a number of subcomponents with separate
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copyright notices and license terms - please see the file ACKNOWLEDGEMENTS.
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The ml-fastvlm model weights copyright and license terms can be
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found in LICENSE_MODEL file.
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-------------------------------------------------------------------------------
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LICENSE_MODEL
ADDED
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Disclaimer: IMPORTANT: This Apple Machine Learning Research Model is
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specifically developed and released by Apple Inc. ("Apple") for the sole purpose
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of scientific research of artificial intelligence and machine-learning
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technology. “Apple Machine Learning Research Model” means the model, including
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This Apple Machine Learning Research Model is provided to You by
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Apple in consideration of your agreement to the following terms, and your use,
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modification, creation of Model Derivatives, and or redistribution of the Apple
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Machine Learning Research Model constitutes acceptance of this Agreement. If You
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do not agree with these terms, please do not use, modify, create Model
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Derivatives of, or distribute this Apple Machine Learning Research Model or
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not limited to any patent, trademark, and similar intellectual property rights
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worldwide that may be infringed by the Apple Machine Learning Research Model,
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the Model Derivatives or by other works in which the Apple Machine Learning
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Research Model may be incorporated.
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added_tokens.json
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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generation_config.json
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{
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"do_sample": true,
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"temperature": null,
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"top_p": null,
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"transformers_version": "4.39.3"
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}
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merges.txt
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preprocessor_config.json
ADDED
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@@ -0,0 +1,28 @@
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| 1 |
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{
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| 2 |
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"crop_size": {
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| 3 |
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"height": 1024,
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"width": 1024
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},
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"do_center_crop": true,
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| 7 |
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"do_convert_rgb": true,
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| 8 |
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"do_normalize": true,
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| 9 |
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"do_rescale": true,
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| 10 |
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"do_resize": true,
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| 11 |
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"image_mean": [
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| 12 |
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0.0,
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| 13 |
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0.0,
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| 14 |
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0.0
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| 15 |
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],
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| 16 |
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"image_processor_type": "CLIPImageProcessor",
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| 17 |
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"image_std": [
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| 18 |
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1.0,
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| 19 |
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1.0,
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| 20 |
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1.0
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| 21 |
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],
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| 22 |
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"processor_class": "LlavaProcessor",
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| 23 |
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"resample": 3,
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| 24 |
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"rescale_factor": 0.00392156862745098,
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| 25 |
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"size": {
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| 26 |
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"shortest_edge": 1024
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| 27 |
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}
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| 28 |
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}
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special_tokens_map.json
ADDED
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@@ -0,0 +1,31 @@
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| 1 |
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{
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| 2 |
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"additional_special_tokens": [
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| 3 |
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"<|im_start|>",
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| 4 |
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"<|im_end|>",
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| 5 |
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"<|object_ref_start|>",
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| 6 |
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"<|object_ref_end|>",
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| 7 |
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"<|box_start|>",
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| 8 |
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"<|box_end|>",
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| 9 |
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"<|quad_start|>",
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| 10 |
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"<|quad_end|>",
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| 11 |
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"<|vision_start|>",
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| 12 |
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"<|vision_end|>",
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| 13 |
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"<|vision_pad|>",
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| 14 |
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"<|image_pad|>",
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| 15 |
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"<|video_pad|>"
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| 16 |
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],
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| 17 |
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"eos_token": {
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| 18 |
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"content": "<|im_end|>",
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| 19 |
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"lstrip": false,
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| 20 |
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"normalized": false,
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| 21 |
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"rstrip": false,
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| 22 |
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"single_word": false
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| 23 |
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},
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| 24 |
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"pad_token": {
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| 25 |
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"content": "<|endoftext|>",
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| 26 |
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"lstrip": false,
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| 27 |
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"normalized": false,
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| 28 |
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"rstrip": false,
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| 29 |
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"single_word": false
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| 30 |
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}
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| 31 |
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}
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vocab.json
ADDED
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