slices:
  - sources:
      - model: Gille/StrangeMerges_10-7B-slerp
        layer_range: [0, 32]
      - model: mlabonne/NeuralBeagle14-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: Gille/StrangeMerges_10-7B-slerp
parameters:
  t:
    - filter: self_attn
      value: [0.1, 0.5, 0.3, 0.6, 0.9]
    - filter: mlp
      value: [0.9, 0.5, 0.7, 0.4, 0.1]
    - value: 0.5
dtype: bfloat16

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Gille/StrangeMerges_11-7B-slerp"
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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	74.80
AI2 Reasoning Challenge (25-Shot)	72.53
HellaSwag (10-Shot)	88.20
MMLU (5-Shot)	65.04
TruthfulQA (0-shot)	69.81
Winogrande (5-shot)	82.32
GSM8k (5-shot)	70.89