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---
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
license: apache-2.0
datasets:
- multilingual/orca_dpo_pairs
- Kukedlc/Big-Spanish-1.2M
language:
- es
---
# NeuralQwen-2.5-1.5B-Spanish
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"])
``` |