AELLM_-_gemma-2-lyco-infinity-9b-gguf

Quantization made by Richard Erkhov.

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gemma-2-lyco-infinity-9b - GGUF

• Model creator: https://huggingface.co/AELLM/
• Original model: https://huggingface.co/AELLM/gemma-2-lyco-infinity-9b/

Original model description:

base_model:

• recoilme/recoilme-gemma-2-9B-v0.4
• BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference library_name: transformers tags:
• merge
• mergekit language:
• en

---

Gemma 2 Lyco Infinity 9B

Gemma 2 Lyco Infinity 9B is a merge of the following models using Mergekit:

• recoilme/recoilme-gemma-2-9B-v0.4
• BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference

🧩 Configuration

base_model: recoilme/recoilme-gemma-2-9B-v0.4
models:
  - model: recoilme/recoilme-gemma-2-9B-v0.4
    # No parameters necessary for base model
  - model: BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference
    parameters:
      density: 0.525
      weight: 1
merge_method: dare_ties
tokenizer_source: base
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "AELLM/gemma-2-lyco-infinity-9b"
messages = [{"role": "user", "content": "You're an AI assistant for a time traveler. How do you help them blend into different historical eras without causing any time paradoxes?"}]

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"])