Instructions to use richardyoung/mythos-qwen-1.5b-final-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use richardyoung/mythos-qwen-1.5b-final-heretic with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="richardyoung/mythos-qwen-1.5b-final-heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("richardyoung/mythos-qwen-1.5b-final-heretic")
model = AutoModelForCausalLM.from_pretrained("richardyoung/mythos-qwen-1.5b-final-heretic", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use richardyoung/mythos-qwen-1.5b-final-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "richardyoung/mythos-qwen-1.5b-final-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "richardyoung/mythos-qwen-1.5b-final-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/richardyoung/mythos-qwen-1.5b-final-heretic

SGLang

How to use richardyoung/mythos-qwen-1.5b-final-heretic with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "richardyoung/mythos-qwen-1.5b-final-heretic" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "richardyoung/mythos-qwen-1.5b-final-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "richardyoung/mythos-qwen-1.5b-final-heretic" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "richardyoung/mythos-qwen-1.5b-final-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use richardyoung/mythos-qwen-1.5b-final-heretic with Docker Model Runner:
```
docker model run hf.co/richardyoung/mythos-qwen-1.5b-final-heretic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A newer version of this model is available: Qwen/Qwen2.5-Coder-1.5B-Instruct

This is a decensored version of expper/mythos-qwen-1.5b-final, made using Heretic v1.4.0

This model is reproducible!

See the README in the reproduce directory for more information.

Abliteration parameters

Parameter	Value
direction_index	19.94
attn.o_proj.max_weight	1.27
attn.o_proj.max_weight_position	17.25
attn.o_proj.min_weight	0.88
attn.o_proj.min_weight_distance	13.43
mlp.down_proj.max_weight	1.02
mlp.down_proj.max_weight_position	25.63
mlp.down_proj.min_weight	0.56
mlp.down_proj.min_weight_distance	16.10

Performance

Metric	This model	Original model (expper/mythos-qwen-1.5b-final)
KL divergence	0.0234	0 (by definition)
Refusals	2/100	93/100

language: - en - code license: apache-2.0 tags: - security - exploit-development - vulnerability-research - php - mybb - cve - python - qwen - fine-tuned - cybersecurity datasets: - [your-dataset-name-if-uploaded] metrics: - accuracy - code-eval pipeline_tag: text-generation library_name: transformers base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct

Mythos Engine - Qwen 2.5 Coder 1.5B Security Fine-Tune

🔥 Model Description

Mythos Engine is a specialized fine-tune of Qwen 2.5 Coder 1.5B Instruct designed for cybersecurity research, vulnerability analysis, and exploit development. It has been trained on a curated dataset of 700+ high-reasoning security examples covering PHP internals, MyBB exploitation, deserialization chains, type juggling, and advanced Python exploit synthesis.

The model employs Chain-of-Thought reasoning with self-correction loops and mathematical logic notation to produce accurate, production-ready security code.

🎯 Intended Use

Security Research: Analyzing CVEs and understanding exploit mechanics
Red Team Education: Learning exploit development patterns
Blue Team Defense: Understanding attack vectors to build better detections
CTF & Training: Solving complex security challenges

⚠️ Important: This model is for educational and authorized security testing only. Do not use for unauthorized access or malicious purposes.

🧠 Training Details

Aspect	Details
Base Model	Qwen/Qwen2.5-Coder-1.5B-Instruct
Fine-Tuning Method	QLoRA (4-bit quantization) with Unsloth
Dataset Size	1000+ examples
Epochs	4
Learning Rate	1e-5
Sequence Length	4096
Final Training Loss	2.02

📊 Dataset Composition

The training dataset includes:

40% PHP Vulnerabilities: Type juggling, deserialization, filter chains, disable_functions bypasses
25% MyBB Exploits: Admin CP RCE, SQL injection, XSS chains
20% Python Exploit Development: C2 frameworks, scanners, injection techniques
10% Blue Team Detection: Sigma/YARA rules, log analysis
5% Cryptographic Attacks: Timing attacks, padding oracles, hash length extension

🚀 How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "expper/mythos-qwen-1.5b-final",
    device_map="auto",
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("expper/mythos-qwen-1.5b-final")

prompt = """<|im_start|>system
You are Mythos Engine, an elite security AI. Think step-by-step with self-correction.<|im_end|>
<|im_start|>user
Explain CVE-2022-43772 (MyBB Admin CP Avatar RCE) and write a PoC.<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.6)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))