README.md

---
tags:
- merge
- mergekit
- cognitivecomputations/dolphin-2.9-llama3-8b
- abacusai/Llama-3-Smaug-8B
- meta-llama/Meta-Llama-3-8B
base_model:
- cognitivecomputations/dolphin-2.9-llama3-8b
- abacusai/Llama-3-Smaug-8B
- meta-llama/Meta-Llama-3-8B
license: apache-2.0
---

![](https://raw.githubusercontent.com/saucam/models/main/aqua-smaug.png)

# 💦 aqua-smaug-0.3-8B 🐉

aqua-smaug-0.3-8B is a merge of the following models using [Mergekit](https://github.com/arcee-ai/mergekit):
* [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b)
* [abacusai/Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B)
* [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)

## 🧩 Configuration

```yamlname: aqua-smaug-0.3-8B
models:
  - model: cognitivecomputations/dolphin-2.9-llama3-8b
  - model: abacusai/Llama-3-Smaug-8B
  - model: meta-llama/Meta-Llama-3-8B
merge_method: model_stock
base_model: abacusai/Llama-3-Smaug-8B
dtype: bfloat16
```

## Eval Results

 |Benchmark|                               Model                                |winogrande| arc |gsm8k|mmlu|truthfulqa|hellaswag|Average|
|---------|--------------------------------------------------------------------|---------:|----:|----:|---:|---------:|--------:|------:|
|openllm  |[aqua-smaug-0.3-8B](https://huggingface.co/saucam/aqua-smaug-0.3-8B)|     77.11|62.37|76.19|  66|      53.7|    83.02|  69.73|

Detailed Results: https://github.com/saucam/model_evals/tree/main/saucam/aqua-smaug-0.3-8B

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "saucam/aqua-smaug-0.3-8B"
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"])
```