---
tags:
- merge
- mergekit
- lazymergekit
- paulml/OGNO-7B
- bardsai/jaskier-7b-dpo-v4.3
base_model:
- paulml/OGNO-7B
- bardsai/jaskier-7b-dpo-v4.3
license: apache-2.0
---
![thumbnail](thumb.webp)
# ramonda-7b-dpo-ties

ramonda-7b-dpo-ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [paulml/OGNO-7B](https://huggingface.co./paulml/OGNO-7B)
* [bardsai/jaskier-7b-dpo-v4.3](https://huggingface.co./bardsai/jaskier-7b-dpo-v4.3)

## Benchmark 
[Open LLM Leaderboard](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard)
| Model                  | Average | ARC  | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|------------------------|--------:|-----:|----------:|-----:|-----------:|-----------:|------:|
| mayacinka/ramonda-7b-dpo-ties |   76.19 | 72.7 |      89.69| 64.5 |      77.17 |      84.77 |  68.92|

[LLM AutoEval](https://gist.github.com/majacinka/370282a808a21b28bacd2c76a998da8f)
| Model                | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
|----------------------|---------|---------|------------|----------|---------|
| ramonda-7b-dpo-ties  | 44.67   | 77.16   | 77.6       | 49.06    | 62.12   |



## 🧩 Configuration

```yaml
models:
  - model: bardsai/jaskier-7b-dpo-v5.6
    # no parameters necessary for base model
  - model: paulml/OGNO-7B
    parameters:
      density: 0.9
      weight: 0.5
  - model: bardsai/jaskier-7b-dpo-v4.3
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: bardsai/jaskier-7b-dpo-v5.6
parameters:
  normalize: true
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mayacinka/ramonda-7b-dpo-ties"
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"])
```