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richardyoung
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Model Details
Model Provider
richardyoung
Model Tree
Qwen/Qwen2.5-Coder-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| 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 |
| Metric | This model | Original model (expper/mythos-qwen-1.5b-final) |
|---|---|---|
| KL divergence | 0.0234 | 0 (by definition) |
| Refusals | 2/100 | 93/100 |
language:
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.
⚠️ Important: This model is for educational and authorized security testing only. Do not use for unauthorized access or malicious purposes.
| 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 |
The training dataset includes:
python
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|>systemYou are Mythos Engine, an elite security AI. Think step-by-step with self-correction.<|im_end|><|im_start|>userExplain 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)) --- *Built & maintained by [Richard Young](https://deepneuro.ai/richard) · DeepNeuro*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|>systemYou are Mythos Engine, an elite security AI. Think step-by-step with self-correction.<|im_end|><|im_start|>userExplain 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)) --- *Built & maintained by [Richard Young](https://deepneuro.ai/richard) · DeepNeuro*