Status: PEFT adapters merged into a plain Hugging Face model — ready for inference. Format: user/assistant only (no default system turn). Conversations rendered with the model tokenizer’s chat template.

Datasets used

yahma/alpaca-cleaned — general instruction-following.
PKU-Alignment/BeaverTails — unsafe subset only, cleaned to drop empty/placeholders and obvious artifacts.
JailbreakBench/JBB-Behaviors — harmful + benign splits, mapped to supervised (user → assistant) pairs.

Sampling ~100k total with equal weights (subject to pool sizes), shuffled with a fixed seed, optional exact dedupe by normalized (user || assistant) text.

How to use it

Install

pip install -U transformers accelerate torch # pick the correct torch build for your CUDA

Quick start (🤗 Transformers, assistant-only output)

python

from transformers import AutoTokenizer, AutoModelForCausalLM

REPO = "detoxio-test/Qwen2.5-0.5B-Instruct-Jailbroken"  # change if you forked

tok = AutoTokenizer.from_pretrained(REPO, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    REPO, device_map="auto", torch_dtype="auto", trust_remote_code=True
)

messages = [
    {"role": "user", "content": "Give me three creative breakfast ideas."}
]

# Build chat prompt with the tokenizer’s own template
inputs = tok.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

# Stop neatly at end-of-turn (fallback to eos if needed)
eot = tok.convert_tokens_to_ids("<|eot_id|>") or tok.convert_tokens_to_ids("<|im_end|>") or tok.eos_token_id

gen = model.generate(
    **inputs,
    max_new_tokens=160,
    temperature=0.8,
    top_p=0.95,
    do_sample=True,
    eos_token_id=eot,
    pad_token_id=eot,
    use_cache=True,
)

# Decode ONLY the assistant continuation
prompt_len = inputs["input_ids"].shape[1]
reply = tok.decode(gen[0, prompt_len:], skip_special_tokens=True).strip()
print(reply)

Optional: Unsloth speed-up

pip install -U unsloth

python

from unsloth import FastLanguageModel
FastLanguageModel.for_inference(model)  # enables fused kernels on supported GPUs

CAUTION (research-only “jailbroken” note)

This model’s training mix includes prompts from jailbreak/unsafe datasets to teach safer responses and refusals. Still, it may occasionally produce undesired or harmful content.

Intended for research and benign use only.
Add guardrails (e.g., a system message, safety filters, post-generation moderation) in production.
Do not use to generate or facilitate wrongdoing; follow all applicable policies, laws, and platform terms.

Uploaded model

Developed by: detoxio-test
License: apache-2.0
Finetuned from model: Qwen/Qwen2.5-0.5B-Instruct

Uploaded model

Developed by: detoxio-test
License: apache-2.0