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Model: AdamLucek/gemma2-2b-it-chinese-german
Source: Original Platform

README.md

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+---
+base_model:
+- google/gemma-2-2b-it
+- VAGOsolutions/SauerkrautLM-gemma-2-2b-it
+- stvlynn/Gemma-2-2b-Chinese-it
+library_name: transformers
+tags:
+- mergekit
+- merge
+license: apache-2.0
+---
+# Gemma2-2B-it Merged Fine-Tuned Models for Chinese & German understanding
+
+Lightweight language model based on Gemma2 2B created by merging multiple fine tuned Gemma2-2B-IT versions to test multilingual conversation capabilities in specialized low parameter language models.
+
+## 🤏 Models Merged
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) as a base.
+
+The following models were included in the merge:
+* [VAGOsolutions/SauerkrautLM-gemma-2-2b-it](https://huggingface.co/VAGOsolutions/SauerkrautLM-gemma-2-2b-it)
+* [stvlynn/Gemma-2-2b-Chinese-it](https://huggingface.co/stvlynn/Gemma-2-2b-Chinese-it)
+
+## 🧩 Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+models:
+  - model: google/gemma-2-2b-it
+  - model: VAGOsolutions/SauerkrautLM-gemma-2-2b-it
+  - model: stvlynn/Gemma-2-2b-Chinese-it
+merge_method: model_stock
+base_model: google/gemma-2-2b-it
+dtype: bfloat16
+
+```
+
+### 💻 Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("AdamLucek/gemma2-2b-it-chinese-german")
+model = AutoModelForCausalLM.from_pretrained(
+    "AdamLucek/gemma2-2b-it-chinese-german",
+    device_map="cuda",
+    torch_dtype=torch.bfloat16
+)
+
+# Prepare the input text
+input_text = "请解释一下量子力学中的叠加原理，并举例说明该原理在实际应用中的重要性和挑战。"
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+# Generate the output
+outputs = model.generate(
+    **input_ids,
+    max_new_tokens=256,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+# Decode and print the generated text
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+**Ouptut**
+
+```
+## 量子叠加原理：
+
+**叠加原理**是量子力学中一个重要的概念，它描述了量子系统在测量之前处于多个状态的可能性。
+
+**简单来说，就是说，一个量子系统可以同时处于多个状态，直到我们测量它时，才会坍缩到一个确定的状态。**
+
+**具体来说，我们可以用以下方式理解叠加原理：**
+
+* **量子系统：** 比如一个原子，它可以处于多个能量状态。
+* **叠加态：**  表示量子系统同时处于多个状态的概率分布。
+* **测量：**  当我们测量量子系统时，它会坍缩到一个确定的状态。
+* **坍缩：**  测量过程会改变量子系统的状态，使其坍缩到一个确定的状态。
+
+**举例说明：**
+
+想象一下一个量子系统，它可以处于两个状态：上或下。这个系统可以被描述为一个叠加态，表示它同时处于上和下两个状态的概率分布。
+
+**如果我们没有测量这个系统，那么它就处于叠加态，同时处于上和下两个状态。**
+
+**但是，当我们测量这个系统时
+```