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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 72b 🐬 |
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Curated and trained by Quan Nguyen (qnguyen3/stablequan), Eric Hartford, and Cognitive Computations |
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[](https://discord.gg/cognitivecomputations) |
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Discord: https://discord.gg/cognitivecomputations |
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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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- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node used for training |
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- [TensorWave](https://tensorwave.com/) - provided 8x mi300x node used for 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 = 'fne/dolphin-llava-qwen2-72b' |
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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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``` |
