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README.md

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+---
+tags:
+- merge
+- mergekit
+- lazymergekit
+- argilla/CapybaraHermes-2.5-Mistral-7B
+- WizardLM/WizardMath-7B-V1.1
+base_model:
+- argilla/CapybaraHermes-2.5-Mistral-7B
+- WizardLM/WizardMath-7B-V1.1
+---
+
+# 試製-暮光-4x7B
+
+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：
+* [argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B)
+* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
+
+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）。
+
+
+# shizhi-twilight-7B
+
+shizhi-twilight-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B)
+* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
+
+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.
+
+## 🧩 Configuration
+
+```yaml
+models:
+  - model: MediaTek-Research/Breeze-7B-Instruct-v0_1
+    # No parameters necessary for base model
+  - model: argilla/CapybaraHermes-2.5-Mistral-7B
+    parameters:
+      density: 0.53
+      weight: 0.65
+  - model: WizardLM/WizardMath-7B-V1.1
+    parameters:
+      density: 0.53
+      weight: 0.35
+merge_method: dare_ties
+base_model: MediaTek-Research/Breeze-7B-Instruct-v0_1
+parameters:
+  int8_mask: true
+dtype: bfloat16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "lipcut/shizhi-twilight-7B"
+messages = [{"role": "user", "content": "什麼是大型語言模型?"}]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```