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. This model was merged using the Model Stock merge method using google/gemma-2-2b-it as a base.

The following models were included in the merge:

🧩 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