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README.md
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---
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license: other
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license_name: tongyi-qianwen
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base_model: Qwen/Qwen2-72B
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tags:
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- generated_from_trainer
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- axolotl
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datasets:
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- cognitivecomputations/Dolphin-2.9
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- teknium/OpenHermes-2.5
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- m-a-p/CodeFeedback-Filtered-Instruction
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- cognitivecomputations/dolphin-coder
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- cognitivecomputations/samantha-data
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- microsoft/orca-math-word-problems-200k
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- Locutusque/function-calling-chatml
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- internlm/Agent-FLAN
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---
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# DolphinVision 7b 🐬
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Curated and trained by Quan Nguyen (qnguyen3/stablequan), Eric Hartford, and Cognitive Computations
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[](https://discord.gg/h3K4XGj2RH)
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Discord: https://discord.gg/h3K4XGj2RH
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/DBGu4dJ95RHHN3yOEuXuP.png" width="600" />
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Our appreciation for the sponsors of DolphinVision:
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- [TensorWave](https://tensorwave.com/) - provided 8x mi300x node used for training, evaluations, and inference
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DolphinVision is a multimodal model. It is uncensored, and capable to reason and comment regarding images that other popular models would object to.
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```python
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import torch
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import transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from PIL import Image
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import warnings
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# disable some warnings
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transformers.logging.set_verbosity_error()
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transformers.logging.disable_progress_bar()
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warnings.filterwarnings('ignore')
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# set device
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torch.set_default_device('cuda') # or 'cpu'
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model_name = 'cognitivecomputations/dolphin-vision-7b'
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# create model
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float16,
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device_map='auto',
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trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained(
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model_name,
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trust_remote_code=True)
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# text prompt
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prompt = 'Describe this image in detail'
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messages = [
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{"role": "user", "content": f'<image>\n{prompt}'}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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print(text)
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text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
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input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)
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# image, sample images can be found in images folder
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image = Image.open('/path/to/image.png')
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image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)
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# generate
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output_ids = model.generate(
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input_ids,
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images=image_tensor,
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max_new_tokens=2048,
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use_cache=True)[0]
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print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())
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```
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## Examples
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```
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<|im_start|>user
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: do OCR on all the text
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<|im_start|>assistant
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| 99 |
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: The text in the image reads as follows:
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| 100 |
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| 101 |
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- STOP USING DOCKER
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| 102 |
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- Applications were not meant to be trapped in virtual jars like digital fireflies
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| 103 |
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- Years of software development, yet no one questioned why we were putting programs in imaginary boats
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- Let's containerize our monolith. I'll just quickly spin up 17 microservices. Statements dreamt up by the utterly deranged
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- Observe the whale-sized deception they fooled us with all this time
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- These are real images used to explain Docker
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- "Yay Matey, I'd like to run my website in a virtual sea"
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- They have played us for absolute fools
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This text is a satirical commentary on the use of Docker in software development, using humor and exaggeration to question the practice.
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```
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## Benchmarks
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TBD
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