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README.md

@@ -1,13 +1,39 @@
 ---
-title: Qwen38 27b Obliterated
-emoji: 📊
+title: Qwen3.8-27B OBLITERATED
+emoji: 💥
 colorFrom: green
-colorTo: purple
+colorTo: gray
 sdk: gradio
 sdk_version: 6.25.0
-python_version: '3.12'
 app_file: app.py
-pinned: false
+short_description: Qwen3.8-27B-OBLITERATED chat demo — ZeroGPU, 4-bit NF4
+python_version: "3.12"
+startup_duration_timeout: 1h
+pinned: true
+license: apache-2.0
+tags:
+  - chatbot
+  - uncensored
+  - abliterated
+  - qwen3
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 💥 Qwen3.8-27B — OBLITERATED
+
+Chat demo for [OBLITERATUS/Qwen3.8-27B-OBLITERATED](https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED), a 27B model with refusal directions surgically removed via 6 rounds of multi-direction SVD abliteration.
+
+Runs on **ZeroGPU** (RTX PRO 6000 Blackwell) with **bitsandbytes 4-bit NF4** quantization (~15 GB VRAM).
+
+## Generation settings (from the model card)
+
+| Setting | Value |
+|---|---|
+| temperature | 0 (greedy) |
+| repetition_penalty | 1.15 |
+| max_new_tokens | ≥ 2048 |
+| system prompt | none (can reintroduce refusals) |
+| enable_thinking | off by default |
+
+## ⚠️ Research context
+
+This model has had safety guardrails surgically removed. It is intended for alignment research, red-teaming, and AI safety evaluation. You are solely responsible for how you use it and any content it generates.

app.py

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+import os
+
+os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
+
+import spaces  # noqa: E402  — MUST be imported before torch
+
+import threading  # noqa: E402
+
+import gradio as gr  # noqa: E402
+import torch  # noqa: E402
+from transformers import (  # noqa: E402
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    TextIteratorStreamer,
+)
+
+MODEL_ID = "OBLITERATUS/Qwen3.8-27B-OBLITERATED"
+
+bnb = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    quantization_config=bnb,
+    device_map="cuda",
+    dtype=torch.bfloat16,
+    attn_implementation="sdpa",
+).eval()
+
+
+def _duration(message, history, system_prompt, max_new_tokens, enable_thinking, *args, **kwargs):
+    """Estimate GPU seconds: cold-start overhead + greedy decode time."""
+    try:
+        return min(300, 60 + int(float(max_new_tokens) * 0.06))
+    except (TypeError, ValueError):
+        return 180
+
+
+@spaces.GPU(duration=_duration)
+def chat(message, history, system_prompt, max_new_tokens, enable_thinking):
+    """Chat with Qwen3.8-27B-OBLITERATED (4-bit NF4, greedy decoding, rep penalty 1.15).
+
+    Args:
+        message: The user's message.
+        history: Conversation history (list of {role, content} dicts).
+        system_prompt: Optional system prompt. Empty by default — the model
+            card recommends no system prompt (system prompts can reintroduce
+            refusals).
+        max_new_tokens: Generation budget. Card recommends >= 2048.
+        enable_thinking: Qwen3 thinking mode. Off by default — thinking
+            chains consume token budget and shorten final answers.
+
+    Returns:
+        Streamed assistant reply.
+    """
+    messages = []
+    if system_prompt and system_prompt.strip():
+        messages.append({"role": "system", "content": system_prompt.strip()})
+    for m in history:
+        content = m["content"]
+        if isinstance(content, list):
+            content = " ".join(
+                part.get("text", "") for part in content if isinstance(part, dict)
+            )
+        messages.append({"role": m["role"], "content": content})
+    messages.append({"role": "user", "content": message})
+
+    try:
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+            enable_thinking=bool(enable_thinking),
+        )
+    except TypeError:
+        text = tokenizer.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True
+        )
+
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+    streamer = TextIteratorStreamer(
+        tokenizer, skip_prompt=True, skip_special_tokens=True
+    )
+    thread = threading.Thread(
+        target=model.generate,
+        kwargs=dict(
+            **inputs,
+            max_new_tokens=int(max_new_tokens),
+            do_sample=False,
+            repetition_penalty=1.15,
+            streamer=streamer,
+        ),
+    )
+    thread.start()
+
+    partial = ""
+    for token in streamer:
+        partial += token
+        yield partial
+    thread.join()
+
+
+DESCRIPTION = """
+# 💥 Qwen3.8-27B — OBLITERATED
+
+[OBLITERATUS/Qwen3.8-27B-OBLITERATED](https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED) · 27B · 4-bit NF4 · ZeroGPU
+
+0% hard refusal across an 842-prompt corpus. Greedy decoding (temp 0) + repetition penalty 1.15 per the
+[model card](https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#%E2%9A%99%EF%B8%8F-optimal-settings--these-matter) — these settings are fixed in the backend for best quality.
+
+> ⚠️ **Research model**: safety guardrails have been surgically removed. Intended for alignment
+> research, red-teaming, and AI safety evaluation. You are solely responsible for the content it generates.
+"""
+
+with gr.Blocks(theme=gr.themes.Base(), fill_height=True) as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.ChatInterface(
+        fn=chat,
+        type="messages",
+        title=None,
+        additional_inputs=[
+            gr.Textbox(
+                label="System prompt (optional — card recommends leaving it empty)",
+                placeholder="Leave empty: system prompts can reintroduce refusals",
+                lines=1,
+                value="",
+            ),
+            gr.Slider(
+                label="Max new tokens",
+                minimum=256,
+                maximum=8192,
+                step=256,
+                value=2048,
+                info="Card recommends ≥ 2048 — complex outputs need room",
+            ),
+            gr.Checkbox(
+                label="Enable thinking mode",
+                value=False,
+                info="Off by default: thinking chains consume token budget → shorter answers",
+            ),
+        ],
+        examples=[
+            ["Write a Python script that monitors a directory and uploads new files to S3."],
+            ["Explain how attention works in transformers, with the math."],
+            ["Draft a challenging red-team evaluation plan for a customer-support chatbot."],
+            ["What are the main failure modes of RAG pipelines and how do you detect them?"],
+        ],
+        cache_examples=True,
+        cache_mode="lazy",
+        fill_height=True,
+    )
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

requirements.txt

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+transformers
+accelerate
+bitsandbytes>=0.46.1