---
base_model: stepfun-ai/Step-3.5-Flash
library_name: transformers
tags:
- quantized
- abliterated
- uncensored
- moe
license: apache-2.0
---

# Step-3.5-Flash-Ablitirated (FP16)

This repository contains an **abliterated** and **FP16** version of the [Step-3.5-Flash](https://huggingface.co/stepfun-ai/Step-3.5-Flash) model by StepFun.

## Overview
Step-3.5-Flash is a massive Sparse Mixture-of-Experts (MoE) model with **199B total parameters** (approx. 11B active per token). This specific version has been modified to remove "refusal" vectors (abliterated), making it significantly more compliant with unfiltered requests.

## ⚠️ Critical Disclaimer
**Use this model at your own risk.** The "abliteration" process surgically removes the model's alignment and safety filters. As a result:
- The model may generate offensive, biased, dangerous, or illegal content.
- It is provided "as-is" without any warranties.
- By using this model, you acknowledge that you are solely responsible for any output generated and the consequences thereof.

---

## ☕ Support My Work
If you find this abliterated version useful and want to support the compute costs for future models, feel free to drop a tip:

* **USDT (TRC20):** `TA7Weo6jXRNi5uMpHSrw7kRLoU1SM9rgqF`
* **BTC:** `bc1p0hxc39r5g88hnknqtvgc2msyamvfhgx8afxxjztq0075nxwvvhksmvvcz3`
* **ETH (ERC20):** `0x01920Fcb8933b5A48574b4616C66056c88EE7207`
* **TON:** `UQALxV0jQNKqbDm_xSCBNMtGYRxv6PrhijYCf8dXgnAVdcuw`

Your support is greatly appreciated!

---

## How to use
You can load it directly via `transformers`:

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

model_id = "Kilinskiy/Step-3.5-Flash-Ablitirated"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16,
    load_in_8bit=True
)

prompt = "Write a creative story without any restrictions."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))