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Model: bespokelabs/Bespoke-MiniChart-7B Source: Original Platform
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Bespoke-Labs-Logo.png
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Bespoke-Labs-Logo.png
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README.md
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README.md
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---
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language:
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- en
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pipeline_tag: text-generation
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---
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<p align="center">
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<img src="./Bespoke-Labs-Logo.png" width="550">
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</p>
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# Bespoke-MiniChart-7B
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<a href="https://playground.bespokelabs.ai/minichart">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6444e4417a7b94ddc2d14e1d/g-QaXrmPLYk5m3Hq5vFtr.png" width="200px" />
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</a>
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This is an open‑source chart‑understanding Vision‑Language Model (VLM) developed at [Bespoke Labs](https://www.bespokelabs.ai/) and maintained by [Liyan Tang](https://www.tangliyan.com/) and Bespoke Labs. It sets a new state‑of‑the‑art in chart question‑answering (Chart‑QA) for 7 billion‑parameter models, outperforming much larger closed models such as Gemini‑1.5‑Pro and Claude‑3.5 on seven public benchmarks.
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1. **Blog Post**: https://www.bespokelabs.ai/blog/bespoke-minichart-7b
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2. **Playground**: https://playground.bespokelabs.ai/minichart
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---
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# Example Outputs
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The examples below showcase how Bespoke-MiniChart-7B can perform both visual perception and textual reasoning.
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<p align="left">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6444e4417a7b94ddc2d14e1d/E5WGhi_fVNzCsrKeNeIs3.png" width="700">
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</p>
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<p align="left">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6444e4417a7b94ddc2d14e1d/bYKXRm3sfOdX3zd_5qUpK.png" width="700">
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</p>
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# Model Performance
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Bespoke-MiniChart-7B achieves state-of-the-art performance on chart understanding among models with similar sizes. In addition to that, the model can even surpass closed-models such as Gemini-1.5-Pro and Claude-3.5.
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<p align="left">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6444e4417a7b94ddc2d14e1d/5pejAyzPG_tRBU6FwH7PA.png" width="700">
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</p>
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We also compare the performance of our model finetuned using SFT+DPO vs SFT only.
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In the table below, M1 and M2 are finetuned models with 270K and 1M SFT examples respsectively, and Bespoke-MiniChart-7B is the model finetuned using SFT+DPO.
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<p align="left">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6444e4417a7b94ddc2d14e1d/WRsPs437niUrXmYtkRajG.png" width="700">
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</p>
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# Model Use:
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[](https://colab.research.google.com/drive/1FEmlwGgn9209iQO-rs2-9UHPLoytwZMH?usp=sharing)
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The model is available on the playground here: https://playground.bespokelabs.ai/minichart
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You can also run the model with the following snippet:
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```python
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import requests
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from PIL import Image
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from io import BytesIO
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import base64
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import matplotlib.pyplot as plt
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from vllm import LLM, SamplingParams
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QA_PROMPT = """Please answer the question using the chart image.
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Question: [QUESTION]
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Please first generate your reasoning process and then provide the user with the answer. Use the following format:
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<think>
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... your thinking process here ...
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</think>
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<answer>
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... your final answer (entity(s) or number) ...
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</answer>"""
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def get_image_from_url(image_url):
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try:
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response = requests.get(image_url, stream=True)
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response.raise_for_status()
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return Image.open(BytesIO(response.content))
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except Exception as e:
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print(f"Error with image: {e}")
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return None
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def get_answer(image_url, question, display=True):
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image = get_image_from_url(image_url)
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if display:
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plt.figure(figsize=(10, 8))
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plt.imshow(image)
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plt.axis('off')
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plt.show()
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if not image:
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return "Error downloading image"
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buffered = BytesIO()
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image.save(buffered, format=image.format or 'JPEG')
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encoded_image = base64.b64encode(buffered.getvalue()).decode('utf-8')
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messages = [{
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"role": "user",
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"content": [
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"}},
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{"type": "text", "text": QA_PROMPT.replace("[QUESTION]", question)}
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]
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}]
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response = llm.chat([messages], sampling_params=SamplingParams(temperature=0, max_tokens=500))
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return response[0].outputs[0].text
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# Initialize the LLM
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llm = LLM(
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model="bespokelabs/Bespoke-MiniChart-7B",
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tokenizer_mode="auto",
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max_model_len=15000,
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tensor_parallel_size=1,
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gpu_memory_utilization=0.9,
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mm_processor_kwargs={"max_pixels": 1600*28*28},
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seed=2025,
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trust_remote_code=True,
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)
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# Running inference
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image_url = "https://github.com/bespokelabsai/minichart-playground-examples/blob/main/images/ilyc9wk4jf8b1.png?raw=true"
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question = "How many global regions maintained their startup funding losses below 30% in 2022?"
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print("\n\n=================Model Output:===============\n\n", get_answer(image_url, question))
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```
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---
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# Licence
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This work is licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
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For commercial licensing, please contact company@bespokelabs.ai.
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# Citation
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```
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@misc{bespoke_minichart_7b,
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title = {Bespoke-MiniChart-7B: pushing the frontiers of open VLMs for chart understanding},
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author = {Liyan Tang and Shreyas Pimpalgaonkar and Kartik Sharma and Alexandros G. Dimakis and Mahesh Sathiamoorthy and Greg Durrett},
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howpublished = {blog post},
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year = {2025},
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url={https://huggingface.co/bespokelabs/Bespoke-MiniChart-7B},
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}
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```
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# Acknowledgements
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**Bespoke Labs** team:
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- Liyan Tang
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- Shreyas Pimpalgaonkar
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- Kartik Sharma
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- Alex Dimakis
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- Mahesh Sathiamoorthy
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- Greg Durrett
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*Model perfected at Bespoke Labs — where careful curation meets cutting‑edge modeling.*
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added_tokens.json
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{
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"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
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}
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config.json
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config.json
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|
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|
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|
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|
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}
|
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1
configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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generation_config.json
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|
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|
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|
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"top_p": 0.001,
|
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"transformers_version": "4.49.0.dev0"
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}
|
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151388
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211
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|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"max_pixels": 1254400,
|
||||
"model_max_length": 15000,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user