Update README.md

README.md

@@ -1,3 +1,60 @@
----
-license: apache-2.0
----
+---
+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))