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

.gitattributes

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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
+```

config.json

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+{
+  "_name_or_path": "google/gemma-2-2b-it",
+  "architectures": [
+    "Gemma2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": 50.0,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": [
+    1,
+    107
+  ],
+  "final_logit_softcapping": 30.0,
+  "head_dim": 256,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 2304,
+  "initializer_range": 0.02,
+  "intermediate_size": 9216,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.42.4",
+  "use_cache": true,
+  "vocab_size": 256000
+}

mergekit_config.yml

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+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

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer.model

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