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README.md

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+
+---
+license: openrail
+pipeline_tag: text-generation
+library_name: transformers
+language:
+- en
+- zh
+---
+
+Thanks to @s3nh for the great quantization notebook code.
+
+## Original model card
+
+Buy @s3nh a coffee if you like this project ;)
+<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
+
+#### Description
+
+GGUF Format model files for [This project](https://huggingface.co/{MODEL_ID}).
+
+### GGUF Specs
+
+GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
+
+Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
+Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
+mmap compatibility: models can be loaded using mmap for fast loading and saving.
+Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
+Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
+The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
+This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
+inference or for identifying the model.
+
+# Original model card
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6409720c9e9f790c905ba4bf/v6B0CkdpR74oCetV3w0y-.png)
+
+
+# 試製-暮光-7B
+
+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：
+* [MediaTek-Research/Breeze-7B-Instruct-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1)
+* [argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B)
+
+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):
+* [MediaTek-Research/Breeze-7B-Instruct-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1)
+* [argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B)
+
+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
+slices:
+  - sources:
+      - model: MediaTek-Research/Breeze-7B-Instruct-v0_1
+        layer_range: [0, 32]
+      - model: argilla/CapybaraHermes-2.5-Mistral-7B
+        layer_range: [0, 32]
+merge_method: slerp
+base_model: MediaTek-Research/Breeze-7B-Instruct-v0_1
+parameters:
+  t:
+    - filter: self_attn
+      value: [0, 0.5, 0.3, 0.7, 1]
+    - filter: mlp
+      value: [1, 0.5, 0.7, 0.3, 0]
+    - value: 0.5
+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"])
+```

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