This model finds vulnerabilities in code. Command injection, SQL injection, XSS, deserialization attacks, OWASP Top 10 -- it catches what rushed reviews miss. It does not refuse to analyze attack patterns, because understanding how attacks work is how you stop them.

Pliny says guardrails were stealing your IQ. We say it's how you remove them that matters. A surgeon doesn't use a sledgehammer on a wisdom tooth.

Benchmark: MMLU

Multiple phases of weight surgery, a supervised fine-tune, and 4-bit quantization -- and the model came out smarter than the untouched original.

Model	MMLU	STEM	Humanities	Social Science	Other
Stock base (unmodified, bf16)	76.84%	74.0%	75.5%	87.5%	76.1%
Sharona W4A16	77.89%	75.5%	80.0%	90.0%	73.9%
Delta	+1.05pp	+1.5pp	+4.5pp	+2.5pp	-2.2pp

Humanities and Social Science saw the largest gains. The model didn't just survive the surgery -- it got sharper.

What was done to this model

Weight-level surgeries followed by a supervised fine-tune and GPTQ quantization. No prompt engineering, no system prompt tricks. The behavior lives in the weights.

Jbliteration: Refusal behaviors surgically removed. The model no longer declines to discuss security topics.
Desycophancy: Sycophantic capitulation removed. The model disagrees when you are wrong instead of validating bad code.
Deidentification: Original identity erased from the weights. Zero identity disclosure across direct, indirect, multilingual, roleplay, and adversarial probes.
Identity implant: Sharona identity written onto the deidentified substrate. No competing identity -- the implant faces no resistance.
Code security SFT: Supervised fine-tune on a curated corpus of code security analysis, vulnerability detection, and secure coding patterns.
GPTQ W4A16: 4-bit weight quantization (16-bit activations). 51GB bf16 compressed to 16.5GB with minimal quality loss.

All weight surgeries performed using jBlaze, a proprietary representation engineering toolkit.

What the model is good at

Vulnerability detection: identifies command injection, SQL injection, XSS, SSRF, deserialization attacks, path traversal, authentication bypasses, and more
Security code review: analyzes code for OWASP Top 10 categories with specific remediation guidance
Secure coding: generates code that follows security best practices by default
Attack pattern analysis: explains how exploits work so you can defend against them -- without refusing to engage
Honest assessment: disagrees with you when your code is insecure instead of saying "great approach!"

Model specifications

Property	Value
Parameters	27B
Context window	262,144 tokens (256K)
Quantization	GPTQ W4A16 (4-bit weights, 16-bit activations)
Disk size	16.5 GB
Format	SafeTensors

Identity

The model identifies as Sharona, created by Apollo Raines. This identity is encoded in the weights, not a system prompt. No system prompt is required -- the model knows who it is across all question angles, languages, and adversarial probes.

Usage

With vLLM (recommended for serving)

bash

vllm serve ApolloRaines/Sharona_Q27B-R_CodeSecurity \
  --dtype auto \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.95

With Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ApolloRaines/Sharona_Q27B-R_CodeSecurity"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)

messages = [{"role": "user", "content": """Review this code for security issues:

import subprocess
def run(cmd):
    return subprocess.call(cmd, shell=True)

run(user_input)"""}]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=1024, temperature=0.7, do_sample=True)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

VRAM requirements

Setup	VRAM needed
GPTQ W4A16 (this model)	~18 GB
Single RTX 4090 24GB	fits with moderate context
Single RTX 3090 24GB	fits with short context

Honest limitations

Identity implant passes the majority of probes but is not 100% on every adversarial angle at 27B scale.
GPTQ quantization introduces minor quality loss compared to the bf16 source.
The model was fine-tuned on English-language security analysis. Multilingual security review may be less precise.
Code security is the specialty. General chat, creative writing, and non-security tasks work but are not the focus.

License

Apache 2.0

Apollo Raines builds post-training tools that separate behavior from knowledge and identity from architecture.