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

[![Runs with DeepswapLLM](https://img.shields.io/badge/runs%20with-DeepswapLLM-2496ED)](https://github.com/apolloraines/DeepswapLLM)

**Run this model on a GPU too small to hold it** — full precision, no quantization. [DeepswapLLM](https://github.com/apolloraines/DeepswapLLM) streams layers across GPU, RAM, and disk, and runs up to **4x faster than AirLLM**.

# 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](https://github.com/p-e-w/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](https://huggingface.co/shb777/Llama-3.3-8B-Instruct-128K)

## Usage

```python
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.