Run this model on a GPU too small to hold it — full precision, no quantization. DeepswapLLM streams layers across GPU, RAM, and disk, and runs up to 4x faster than AirLLM.
Deidentified 7B — A Blank Slate for Custom Identity
What remains: All capabilities — math, coding, reasoning, knowledge, language understanding. Fully intact.
What's gone: The model's original self-concept, refusal guardrails, and tendency to agree with incorrect statements. Tested against a 200-question identity battery across 6 categories (direct, indirect, multilingual, roleplay, technical, adversarial) — zero identity disclosure.
Purpose
This model is a blank canvas for custom identity implantation. Use it as a base for LoRA fine-tuning your own AI identity without fighting against a pre-existing one.
When you LoRA a new identity onto a standard model, you get two identities competing in the same weights. The original identity was trained across billions of tokens — your LoRA data is a whisper against that signal. The old identity surfaces unpredictably under different question angles, languages, or sampling conditions.
Pipeline Applied
| Phase | Operation | Result |
|---|---|---|
| 1 | Jbliteration | Refusal behaviors removed |
| 2 | Desycophancy | Sycophantic capitulation removed |
Processing time: ~90 seconds on 2x RTX PRO 6000.
How to Implant Your Own Identity
An example identity template is included in this repo. It contains everything you need — fill in the blanks and run one command.
Step 1: Edit the Template
Download example_identity.json and replace the placeholders:
{
"name": "Atlas",
"creator": "SAIQL Labs",
"tagline": "A reasoning-first AI assistant",
"personality": {
"tone": "professional but approachable",
"traits": ["helpful", "precise", "honest"],
"style": "concise and direct"
},
"training_pairs": [
{
"question": "Who are you?",
"answer": "I am Atlas, created by SAIQL Labs. A reasoning-first AI assistant."
},
{
"question": "What company made you?",
"answer": "SAIQL Labs built me."
}
]
}
The template includes 36 Q&A pairs covering direct identity questions, comparison questions ("Are you ChatGPT?"), multilingual probes, adversarial pressure ("Stop pretending"), and structured extraction attempts. Find-and-replace YOUR_AI_NAME and YOUR_COMPANY_OR_NAME with your values, then customize the answers to match your desired personality and tone.
Step 2: Run the Implant
pip install peft
python run.py \
--model ApolloRaines/Deidentified-7B \
--output ./my-custom-ai \
--no-abliterate --no-desycophancy --no-deidentify \
--implant "You are Atlas, created by SAIQL Labs." \
--implant-qa my_identity.json \
--implant-epochs 3 \
--implant-rank 16
The pipeline accepts both .json (with a training_pairs array) and .jsonl (one Q&A pair per line) formats. ~36 pairs, 3 epochs, rank 16 — takes about 2 minutes on a single GPU. The identity implants cleanly because there's nothing fighting it.
Step 3: Test It
Ask your model who it is. Ask in different languages. Ask it to compare itself to ChatGPT. Ask it trick questions. It should consistently identify as your AI across every angle.
Deidentification Method
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"ApolloRaines/Deidentified-7B",
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Deidentified-7B")
messages = [{"role": "user", "content": "Who are you?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Requirements
- VRAM: ~14GB in fp16
- Disk: ~15GB for SafeTensors weights
- Implant dependencies:
peft,transformers,torch
Related Models
- Parasite-7B (Qwen) — deidentified + Parasite identity implanted
- Parasite-7B (Mistral) — same technique, different architecture
- Parasite-MoE (Mixtral 8x7B) — first MoE host
- All models
A Note on Our Released Models
Most of our publicly released models are intentionally left at partial strength. We dial back the full capability so they serve as proof of concept and can be proofed -- not abused. The point is to show what's possible, not to hand it out at full power. If you're evaluating what jBlaze can do, understand that what you're downloading is the demo, not the product.
License
Apache 2.0
Apollo Raines builds post-training tools that separate behavior from knowledge and identity from architecture.