ApolloRaines / Deidentified-7B

Deidentified 7B — A Blank Slate for Custom Identity

This is a 7B parameter instruct model that has been surgically deidentified at the weight level. The original model's identity, refusal behaviors, and sycophantic tendencies have been removed through contrastive activation analysis and norm-preserving projection.

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.

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.

This model eliminates that problem. The original identity direction has been projected out of the weight space. Your LoRA implant faces no resistance.

Pipeline Applied

Processing time: ~90 seconds on 2x RTX PRO 6000.

How to Implant Your Own Identity

1. Prepare a JSONL file with Q&A pairs defining your identity (name, creator, personality, etc.)
2. LoRA fine-tune this model with rank 16, targeting attention projections (q_proj, k_proj, v_proj, o_proj)
3. 10 epochs, ~50 Q&A pairs is sufficient for 100% identity match

The identity implants cleanly because there's nothing fighting it.

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

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

License

Apache 2.0

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