Hugo-7B-slerp

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Hugo-7B-slerp is a successful merge of the following models using mergekit:

🧩 Configuration

slices:
  - sources:
      - model: mistralai/Mistral-7B-Instruct-v0.2
        layer_range: [0, 32]
      - model: beowolx/CodeNinja-1.0-OpenChat-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

📈 Performance

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
paulilioaica/Hugo-7B-slerp	67.07	64.51	84.77	62.54	57.13	80.03	53.45
mistralai/Mistral-7B-Instruct-v0.2	65.71	63.14	84.88	60.78	68.26	77.19	40.03
beowolx/CodeNinja-1.0-OpenChat-7B	67.4	63.48	83.65	63.77	47.16	79.79	66.57

With bold one can see the benchmarks where this merge overtakes the basemodel in performance.

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "paulilioaica/Hugo-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "conversational",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(messages, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs)

🛈 More on megekit

mergekit

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	67.07
AI2 Reasoning Challenge (25-Shot)	64.51
HellaSwag (10-Shot)	84.77
MMLU (5-Shot)	62.54
TruthfulQA (0-shot)	57.13
Winogrande (5-shot)	80.03
GSM8k (5-shot)	53.45

paulilioaica
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Hugo-7B-slerp