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kuno-dogwalker-7b

🦮🦮🦮🥷

Decent metrics, but writing feels off compared to kuno-royale-v2-7b.

Model Average ARC HellaSwag MMLU TruthfulQA Winogrande GSM8K
eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO 76.45 73.12 89.09 64.80 77.45 84.77 69.45
mlabonne/AlphaMonarch-7B 75.99 73.04 89.18 64.40 77.91 84.69 66.72
core-3/kuno-dogwalker-7b 74.94 72.01 88.17 64.96 71.39 82.00 71.11
core-3/kuno-royale-v2-7b 74.80 72.01 88.15 65.07 71.10 82.24 70.20
core-3/kuno-royale-7B 74.74 71.76 88.20 65.13 71.12 82.32 69.90
SanjiWatsuki/Kunoichi-DPO-v2-7B 72.46 69.62 87.44 64.94 66.06 80.82 65.88
SanjiWatsuki/Kunoichi-7B 72.13 68.69 87.10 64.90 64.04 81.06 67.02

kuno-dogwalker-7b is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
  - sources:
      - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
        layer_range: [0, 32]
      - model: mlabonne/AlphaMonarch-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
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

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "core-3/kuno-dogwalker-7b"
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"])
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