---
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
- Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO
tags:
- merge
- mergekit
- lazymergekit
- Qwen/Qwen2.5-1.5B-Instruct
- Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO
---

# NeuralQwen-2.5-1.5B-Spanish


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d71ab4089bc502ceb44d29/Tu9FV0dQJXz-mlriKNqdE.png)

NeuralQwen-2.5-1.5B-Spanish is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
* [Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO](https://huggingface.co/Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO)

## 🧩 Configuration

```yaml
models:
  - model: Qwen/Qwen2.5-1.5B
    # No parameters necessary for base model
  - model: Qwen/Qwen2.5-1.5B-Instruct
    parameters:
      density: 0.66
      weight: 0.6
  - model: Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO
    parameters:
      density: 0.44
      weight: 0.4
merge_method: dare_ties
base_model: Qwen/Qwen2.5-1.5B
parameters:
  int8_mask: true
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralQwen-2.5-1.5B-Spanish"
messages = [{"role": "user", "content": "What is a large language model?"}]

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"])
```