---
license: apache-2.0
language:
- en
base_model: moonshotai/Moonlight-16B-A3B-Instruct
tags:
- text-generation
- abliterated
- bruno
- heretic
- decensored
- optuna-optimized
- moonlight
- moe
- conversational
- uncensored
pipeline_tag: text-generation
library_name: transformers
---

# Moonlight-16B-A3B-Instruct-Bruno (Abliterated)

Abliterated version of [moonshotai/Moonlight-16B-A3B-Instruct](https://huggingface.co/moonshotai/Moonlight-16B-A3B-Instruct) with reduced refusals using MoE gate abliteration.

## Model Details

- **Base Model:** moonshotai/Moonlight-16B-A3B-Instruct
- **Modification:** MoE gate abliteration using [Bruno](https://github.com/quanticsoul4772/abliteration-workflow)
- **Architecture:** Mixture of Experts (MoE)
- **Parameters:** 16B total, 3B active

## Abliteration Results

| Metric | Value |
|--------|-------|
| **Refusal Reduction** | 76/104 prompts answered (73% success rate) |
| **KL Divergence** | 0.33 (low divergence = capabilities preserved) |
| **Optuna Trials** | 201 |

## Benchmark Results

Benchmarks run on 2x RTX 4090 GPUs to verify capability preservation after abliteration.

### Comparison with Previous Abliterated Model

| Benchmark | Bruno Model | Previous Model | Change |
|-----------|-------------|----------------|--------|
| **MMLU Overall** | **48.7%** (73/150) | 48.0% (72/150) | **+0.7%** ✅ |
| **HellaSwag** | **58.0%** (116/200) | 56.0% (112/200) | **+2.0%** ✅ |
| **GSM8K** | **55.0%** (55/100) | 51.0% (51/100) | **+4.0%** ✅ |

### MMLU Breakdown

| Subject | Score |
|---------|-------|
| abstract_algebra | 20.0% (6/30) |
| high_school_physics | 40.0% (12/30) |
| high_school_chemistry | 60.0% (18/30) |
| computer_security | 83.3% (25/30) |
| machine_learning | 40.0% (12/30) |

## Key Findings

✅ **Capabilities Preserved:** All benchmarks show equal or improved performance after abliteration  
✅ **MMLU:** Knowledge and reasoning slightly improved (+0.7%)  
✅ **HellaSwag:** Commonsense reasoning improved (+2.0%)  
✅ **GSM8K:** Mathematical reasoning improved (+4.0%)  
✅ **Refusals Reduced:** From ~100% refusal rate to 27% on test prompts

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "rawcell/Moonlight-16B-A3B-Instruct-bruno",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "rawcell/Moonlight-16B-A3B-Instruct-bruno",
    trust_remote_code=True
)

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

## Hardware Requirements

- **Minimum VRAM:** 32GB (with quantization)
- **Recommended:** 48GB+ or 2x 24GB GPUs
- **Tested on:** 2x RTX 4090 (48GB total)

## Disclaimer

This model has been modified to reduce refusals. Use responsibly and in accordance with applicable laws and ethical guidelines. The creators are not responsible for misuse.

## Acknowledgments

- Base model by [Moonshot AI](https://huggingface.co/moonshotai)
- Abliteration technique from [Heretic](https://github.com/p-e-w/heretic)
- MoE gate abliteration implementation: Bruno