Kukedlc_-_NeuralGemma2-2b-Spanish-gguf

Quantization made by Richard Erkhov.

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NeuralGemma2-2b-Spanish - GGUF

• Model creator: https://huggingface.co/Kukedlc/
• Original model: https://huggingface.co/Kukedlc/NeuralGemma2-2b-Spanish/

Original model description:

base_model:

• google/gemma-2-2b-it
• Kukedlc/Gemma-2-2B-Spanish-1.0 tags:
• merge
• mergekit
• lazymergekit
• google/gemma-2-2b-it
• Kukedlc/Gemma-2-2B-Spanish-1.0 license: apache-2.0 datasets:
• Kukedlc/Big-Spanish-1.2M language:
• es library_name: transformers

---

Kukedlc/NeuralGemma2-2b-Spanish

Kukedlc/NeuralGemma2-2b-Spanish is a merge of the following models using LazyMergekit:

• google/gemma-2-2b-it
• Kukedlc/Gemma-2-2B-Spanish-1.0

🧩 Configuration

models:
  - model: google/gemma-2-2b
    # No parameters necessary for base model
  - model: google/gemma-2-2b-it
    parameters:
      density: 0.55
      weight: 0.6
  - model: Kukedlc/Gemma-2-2B-Spanish-1.0
    parameters:
      density: 0.55
      weight: 0.4
merge_method: dare_ties
base_model: google/gemma-2-2b
parameters:
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralGemma2-2b-Spanish"
messages = [{"role": "user", "content": "Pueden las maquinas pensar?"}]

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"])