| | ---
|
| | 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
|
| | |
