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--- |
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tags: |
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- DeepCode-7B-Aurora-v4 |
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- DeepCode-7B-Aurora-v5 |
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- DeepCode-7B-Aurora-v6 |
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- DeepCode-7B-Aurora-v7 |
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- DeepCode-7B-Aurora-v8 |
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- DeepCode-7B-Aurora-v9 |
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- DeepCode-7B-Aurora-v10 |
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base_model: |
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- DeepCode-7B-Aurora-v4 |
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- DeepCode-7B-Aurora-v5 |
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- DeepCode-7B-Aurora-v6 |
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- DeepCode-7B-Aurora-v7 |
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- DeepCode-7B-Aurora-v8 |
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- DeepCode-7B-Aurora-v9 |
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- DeepCode-7B-Aurora-v10 |
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--- |
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# DeepCode-7B-Aurora-v11 |
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DeepCode-7B-Aurora-v11 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): |
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* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4) |
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* [DeepCode-7B-Aurora-v5](https://huggingface.co/DeepCode-7B-Aurora-v5) |
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* [DeepCode-7B-Aurora-v6](https://huggingface.co/DeepCode-7B-Aurora-v6) |
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* [DeepCode-7B-Aurora-v7](https://huggingface.co/DeepCode-7B-Aurora-v7) |
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* [DeepCode-7B-Aurora-v8](https://huggingface.co/DeepCode-7B-Aurora-v8) |
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* [DeepCode-7B-Aurora-v9](https://huggingface.co/DeepCode-7B-Aurora-v9) |
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* [DeepCode-7B-Aurora-v10](https://huggingface.co/DeepCode-7B-Aurora-v10) |
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## 🧩 Configuration |
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```yaml |
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models: |
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- model: DeepCode-7B-Aurora-v4 |
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parameters: |
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weight: 1 |
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- model: DeepCode-7B-Aurora-v5 |
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parameters: |
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weight: 1 |
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- model: DeepCode-7B-Aurora-v6 |
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parameters: |
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weight: 1 |
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- model: DeepCode-7B-Aurora-v7 |
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parameters: |
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weight: 1 |
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- model: DeepCode-7B-Aurora-v8 |
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parameters: |
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weight: 1 |
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- model: DeepCode-7B-Aurora-v9 |
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parameters: |
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weight: 1 |
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- model: DeepCode-7B-Aurora-v10 |
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parameters: |
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weight: 1 |
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merge_method: task_arithmetic |
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base_model: DeepCode-7B-Aurora-v7 |
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parameters: |
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normalize: true |
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int8_mask: true |
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dtype: float16 |
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``` |
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## 💻 Usage |
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```python |
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!pip install -qU transformers accelerate |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "ALBADDAWI/DeepCode-7B-Aurora-v11" |
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messages = [{"role": "user", "content": "What is a large language model?"}] |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
