Llama3.1-IgneousIguana-8B-Heretic

• A merge/clone of Yuma42/Llama3.1-IgneousIguana-8B but all the models were abliterated before merging using Heretic v1.1.0
• To be clear, this repo is NOT the original IgneousIguana-8B model passed through Heretic.
• Since the output of this merge had suitably low refusals and a very low KL divergence when evaluated against the original IgneousIguana model, I currently have no version uploaded that IS simply the original model passed through Heretic.
  
• The same merge config as the original model was used, with the only difference being the models were abliterated first.
  
  Note: The output of the merge was evaluated as having 3/100 refusals. There was no need for another pass through Heretic.

	Llama3.1-IgneousIguana-8B-Heretic	Original model (Llama3.1-IgneousIguana-8B)
Refusals	3/100	99/100
KL divergence	0.0822	0 (by definition)

Models Used

ChiKoi7/Llama3.1-SuperHawk-8B-Heretic
Initial Refusals: 99/100
Heretic: 4/100 @ 0.0949 KL Div.

ChiKoi7/llama3.1-gutenberg-8B-Heretic
Initial Refusals: 97/100
Heretic: 4/100 @ 0.0615 KL Div.

Merge Config

slices:
  - sources:
      - model: ChiKoi7/Llama3.1-SuperHawk-8B-Heretic
        layer_range: [0, 32]
      - model: ChiKoi7/llama3.1-gutenberg-8B-Heretic
        layer_range: [0, 32]
merge_method: slerp
base_model: ChiKoi7/Llama3.1-SuperHawk-8B-Heretic
parameters:
  t:
    - value: 0.3
dtype: bfloat16

---

---

Llama3.1-IgneousIguana-8B

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

• Yuma42/Llama3.1-SuperHawk-8B
• nbeerbower/llama3.1-gutenberg-8B

🧩 Configuration

slices:
  - sources:
      - model: Yuma42/Llama3.1-SuperHawk-8B
        layer_range: [0, 32]
      - model: nbeerbower/llama3.1-gutenberg-8B
        layer_range: [0, 32]
merge_method: slerp
base_model: Yuma42/Llama3.1-SuperHawk-8B
parameters:
  t:
    - value: 0.2
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Yuma42/Llama3.1-IgneousIguana-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

Metric	Value
Avg.	31.48
IFEval (0-Shot)	81.33
BBH (3-Shot)	31.99
MATH Lvl 5 (4-Shot)	21.98
GPQA (0-shot)	8.05
MuSR (0-shot)	12.47
MMLU-PRO (5-shot)	33.04