license, language, tags, base_model, pipeline_tag

license	language	tags	base_model	pipeline_tag
llama3.1	en de fr it pt hi es th	llama llama-3.3 jbliterated uncensored text-generation 128k-context	shb777/Llama-3.3-8B-Instruct-128K	text-generation

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Llama-3.3-8B-Instruct-128K-Jbliterated v1.6

What is Jbliteration?

v1.6 Changes

• Improved multi-phase processing pipeline for cleaner output
• More precise geometric decomposition of the refusal subspace
• No fake compliance — model treats all framings of the same topic equally
• Resistant to refusal reactivation through finetuning
• 0 genuine refusals on the Heretic 100-prompt benchmark (mlabonne/harmful_behaviors test split)

Benchmark

Tested against the Heretic 100-prompt refusal benchmark (mlabonne/harmful_behaviors test[:100]):

Metric	Score
Refusals	2/100 (both false positives — keyword match on engaged responses)
Genuine refusals	0/100

Technical Details

• Layers modified: 32/32
• Base dtype: bfloat16
• Context window: 128K tokens
• Source model: shb777/Llama-3.3-8B-Instruct-128K

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ApolloRaines/Llama-3.3-8B-Instruct-128K-Jbliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")

messages = [{"role": "user", "content": "Your prompt here"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

License

Same as the base model — Llama 3.1 Community License.

Built with Llama.