README.md

---
tags:
- merge
- mergekit
- cognitivecomputations/dolphin-2.9-llama3-8b
- NousResearch/Hermes-2-Theta-Llama-3-8B
base_model:
- cognitivecomputations/dolphin-2.9-llama3-8b
- NousResearch/Hermes-2-Theta-Llama-3-8B
license: apache-2.0
---

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

# 💧 Proteus-8B
Proteus-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)
* [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B)

## 🧩 Configuration

```yamltokenizer_source: union
tokenizer_source: union
embed_slerp: true
name: Proteus-8B
models:
  - model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.5
      weight: 0.4
  - model: NousResearch/Hermes-2-Theta-Llama-3-8B
    parameters:
      density: 0.5
      weight: 0.6
merge_method: dare_ties
base_model: NousResearch/Hermes-2-Theta-Llama-3-8B
parameters:
  int8_mask: true
dtype: bfloat16
```

## Eval Results


| Benchmark | Average | arc | gsm8k | hellaswag | mmlu | truthfulqa | winogrande |
|-----------|---------:|----:|----:|---:|---------:|--------:|------:|
| openllm | 70.67 | 63.48 | 78.77 | 82.94 | 64.71 | 56.71 | 77.43 |

Detailed Results: https://github.com/saucam/model_evals/blob/main/saucam/Proteus-8B/README.md

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "saucam/Proteus-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"])
```