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"])