README.md

metadata

base_model:
  - Qwen/Qwen2.5-1.5B-Instruct
  - Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO
datasets:
  - multilingual/orca_dpo_pairs
  - Kukedlc/Big-Spanish-1.2M
language:
  - es
license: apache-2.0
tags:
  - merge
  - mergekit
  - lazymergekit
  - Qwen/Qwen2.5-1.5B-Instruct
  - Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO
  - autoquant
  - gguf

NeuralQwen-2.5-1.5B-Spanish

NeuralQwen-2.5-1.5B-Spanish is a merge of the following models using LazyMergekit:

• Qwen/Qwen2.5-1.5B-Instruct
• Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO

🧩 Configuration

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

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralQwen-2.5-1.5B-Spanish"
messages = [{"role": "system", "content": "Eres un asistente de pensamiento logico que piensa paso a paso, por cada pregunta que te hagan deberes comprobar la respuesta por 3 metodos diferentes."}, 
            {"role": "user", "content": "Cuantas letras 'r' tiene la palabra strawberry?"}]

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"])