How to use from
llama.cpp
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Kukedlc/NeuralGemma-2B-Slerp-GGUF:
# Run inference directly in the terminal:
llama cli -hf Kukedlc/NeuralGemma-2B-Slerp-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Kukedlc/NeuralGemma-2B-Slerp-GGUF:
# Run inference directly in the terminal:
llama cli -hf Kukedlc/NeuralGemma-2B-Slerp-GGUF:
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Kukedlc/NeuralGemma-2B-Slerp-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf Kukedlc/NeuralGemma-2B-Slerp-GGUF:
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Kukedlc/NeuralGemma-2B-Slerp-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Kukedlc/NeuralGemma-2B-Slerp-GGUF:
Use Docker
docker model run hf.co/Kukedlc/NeuralGemma-2B-Slerp-GGUF:
Quick Links

Kukedlc/NeuralGemma2-2b-Spanish

NeuralGemma-2B-Slerp is a merge of the following models using LazyMergekit:

🧩 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

πŸ’» Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralGemma-2B-Slerp"
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"])
Downloads last month
55
GGUF
Model size
3B params
Architecture
gemma2
Hardware compatibility
Log In to add your hardware

4-bit

8-bit

16-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. πŸ™‹ Ask for provider support

Model tree for Kukedlc/NeuralGemma-2B-Slerp-GGUF