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--- |
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tags: |
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- merge |
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- mergekit |
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- lazymergekit |
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- argilla/CapybaraHermes-2.5-Mistral-7B |
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- WizardLM/WizardMath-7B-V1.1 |
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base_model: |
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- argilla/CapybaraHermes-2.5-Mistral-7B |
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- WizardLM/WizardMath-7B-V1.1 |
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--- |
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# 試製-暮光-4x7B |
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試製-暮光-7B 是用[LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing)融合以下模型生成的: |
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* [argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B) |
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* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1) |
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這是一個實驗模型,目的是爲了檢驗套用在不同語言上的高品質模型調教是否能夠轉移(此模型爲英文到中文)。 |
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# shizhi-twilight-7B |
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shizhi-twilight-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): |
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* [argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B) |
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* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1) |
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This is an experiment product on checking whether high quality fine-tuning on one language (English) could be transferred to another language (Mandarin) leveraging Slerp merge method. |
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## 🧩 Configuration |
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```yaml |
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models: |
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- model: MediaTek-Research/Breeze-7B-Instruct-v0_1 |
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# No parameters necessary for base model |
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- model: argilla/CapybaraHermes-2.5-Mistral-7B |
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parameters: |
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density: 0.53 |
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weight: 0.65 |
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- model: WizardLM/WizardMath-7B-V1.1 |
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parameters: |
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density: 0.53 |
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weight: 0.35 |
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merge_method: dare_ties |
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base_model: MediaTek-Research/Breeze-7B-Instruct-v0_1 |
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parameters: |
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int8_mask: true |
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dtype: bfloat16 |
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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 = "lipcut/shizhi-twilight-7B" |
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messages = [{"role": "user", "content": "什麼是大型語言模型?"}] |
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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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``` |