Llama3.1-SuperHawk-8B-Heretic-v2

Llama3.1-SuperHawk-8B-Heretic-v2

• A merge/clone of Llama3.1-SuperHawk-8B but all the models were abliterated before merging using Heretic v1.1.0
• To be clear, this repo is NOT the original SuperHawk model passed through Heretic. A version that IS the original model passed through Heretic can be found here
• The same merge config as the original model was used, with the only difference being the models were abliterated first.<br><br> Note: The output of the merge was evaluated as having 16/100 refusals. The output model was passed through Heretic again to get the final result.

Models Used

ChiKoi7/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base-Heretic<br> Initial Refusals: 97/100<br> Heretic: 3/100 @ 0.0530 KL Div.<br><br>

ChiKoi7/Llama-3.1-Hawkish-8B-Heretic<br> Initial Refusals: 98/100<br> Heretic: 9/100 @ 0.0595 KL Div.<br>

Merge Config

slices:
  - sources:
      - model: ChiKoi7/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base-Heretic
        layer_range: [0, 32]
      - model: ChiKoi7/Llama-3.1-Hawkish-8B-Heretic
        layer_range: [0, 32]
merge_method: slerp
base_model: ChiKoi7/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base-Heretic
parameters:
  t:
    - value: 0.3
dtype: bfloat16

---

---

Llama3.1-SuperHawk-8B is a merge of the following models using LazyMergekit:

• Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
• mukaj/Llama-3.1-Hawkish-8B

🧩 Configuration

slices:
  - sources:
      - model: Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
        layer_range: [0, 32]
      - model: mukaj/Llama-3.1-Hawkish-8B
        layer_range: [0, 32]
merge_method: slerp
base_model: Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
parameters:
  t:
    - value: 0.3
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Yuma42/Llama3.1-SuperHawk-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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here