README.md

metadata

base_model:
  - nbeerbower/gemma2-gutenberg-9B
  - UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
  - princeton-nlp/gemma-2-9b-it-SimPO
tags:
  - merge
  - mergekit
  - lazymergekit
  - nbeerbower/gemma2-gutenberg-9B
  - UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
  - princeton-nlp/gemma-2-9b-it-SimPO

Gemma_Writer-9b

Gemma_Writer-9b is a merge of the following models using LazyMergekit:

• nbeerbower/gemma2-gutenberg-9B
• UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
• princeton-nlp/gemma-2-9b-it-SimPO

🧩 Configuration

models:
  - model: IlyaGusev/gemma-2-9b-it-abliterated
    # No parameters necessary for base model
  - model: nbeerbower/gemma2-gutenberg-9B
    parameters:
      density: 0.6
      weight: 0.4
  - model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
    parameters:
      density: 0.53
      weight: 0.3
  - model: princeton-nlp/gemma-2-9b-it-SimPO
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: IlyaGusev/gemma-2-9b-it-abliterated
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "StoneLabs/Gemma_Writer-9b"
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"])