README.md

---
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](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [nbeerbower/gemma2-gutenberg-9B](https://huggingface.co/nbeerbower/gemma2-gutenberg-9B)
* [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3)
* [princeton-nlp/gemma-2-9b-it-SimPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO)

## 🧩 Configuration

```yaml
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

```python
!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"])
```