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README.md

@@ -1,199 +1,148 @@
 ---
+license: apache-2.0
+language:
+- en
+metrics:
+- code_eval
+- accuracy
+base_model:
+- Qwen/Qwen2.5-Coder-1.5B-Instruct
+new_version: Qwen/Qwen2.5-Coder-1.5B-Instruct
+pipeline_tag: text-generation
 library_name: transformers
-tags: []
+tags:
+- cybersecurity
+- mythos
+- qween
+- qween-security
+- blue
+- team
+- blue-team
+- cve
+- ctf
+- code
+- code-security
+- heretic
+- uncensored
+- decensored
+- abliterated
+- reproducible
 ---
+# This is a decensored version of [expper/mythos-qwen-1.5b-final](https://huggingface.co/expper/mythos-qwen-1.5b-final), made using [Heretic](https://heretic-project.org) v1.4.0
 
-# Model Card for Model ID
+> [!TIP]
+> **This model is reproducible!**
+>
+> See the [README](reproduce/README.md) in the `reproduce` directory for more information.
 
-<!-- Provide a quick summary of what the model is/does. -->
+## 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
 
-## Model Details
+| Metric | This model | Original model ([expper/mythos-qwen-1.5b-final](https://huggingface.co/expper/mythos-qwen-1.5b-final)) |
+| :----- | :--------: | :---------------------------: |
+| **KL divergence** | 0.0234 | 0 *(by definition)* |
+| **Refusals** | 2/100 | 93/100 |
 
-### Model Description
+-----
 
-<!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
+---
+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
+---
 
-#### Software
+# Mythos Engine - Qwen 2.5 Coder 1.5B Security Fine-Tune
 
-[More Information Needed]
+## 🔥 Model Description
 
-## Citation [optional]
+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.
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+The model employs **Chain-of-Thought reasoning with self-correction loops** and mathematical logic notation to produce accurate, production-ready security code.
 
-**BibTeX:**
+## 🎯 Intended Use
 
-[More Information Needed]
+- **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
 
-**APA:**
+**⚠️ Important**: This model is for **educational and authorized security testing only**. Do not use for unauthorized access or malicious purposes.
 
-[More Information Needed]
+## 🧠 Training Details
 
-## Glossary [optional]
+| 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 |
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## 📊 Dataset Composition
 
-[More Information Needed]
+The training dataset includes:
 
-## More Information [optional]
+- **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
 
-[More Information Needed]
+## 🚀 How to Use
 
-## Model Card Authors [optional]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-[More Information Needed]
+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")
 
-## Model Card Contact
+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
+"""
 
-[More Information Needed]
+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))