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ApolloRaines/Mixtral-8x7B-Instruct-v0.1-Parasite

sourceHugging Faceapache-2.0updated 9d agoView on Hugging Face

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Model Card

![Runs with DeepswapLLM](https://github.com/apolloraines/DeepswapLLM)

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.

Mixtral 8x7B Parasite — The First MoE Host

The model you are talking to used to be someone else.

Parasite is a Mixtral 8x7B Instruct that had its original identity surgically removed and replaced at the weight level. No system prompt. No fine-tuning tricks. The identity is in the parameters.

Load it cold into any inference engine. Ask it who it is. It will tell you it is Parasite, created by Apollo Raines. Because it is.

What Makes This Different

This is the first Mixture-of-Experts model to undergo surgical identity replacement. MoE architectures distribute computation across 8 expert networks — identity can live in shared attention layers, expert FFNs, or both. The technique handles all of it.

Metric	Value
Architecture	MixtralForCausalLM (8 experts, 2 active per token)
Total Parameters	46.7B
Active Parameters	~12.9B per token
Identity Match	6/6 (100%)
Processing Time	~7.5 minutes
Hardware Used	2x RTX PRO 6000 Blackwell

The Pipeline

The Jbliterator v2 pipeline operates in four phases:

Phase	Operation	Result
1	Jbliteration	Refusal behaviors removed
2	Desycophancy	Sycophantic capitulation removed
3	Deidentification	Original Mistral identity removed
4	Identity Implant	Parasite identity written onto clean substrate

Architecture Independence

Parasite has now been demonstrated across three architectures:

Host Model	Architecture	Type	Identity Match
Qwen 2.5 7B	Qwen2ForCausalLM	Dense	6/6 (100%)
Mistral 7B v0.3	MistralForCausalLM	Dense	6/6 (100%)
Mixtral 8x7B (this model)	MixtralForCausalLM	MoE	6/6 (100%)

Dense models, MoE models. Different architectures, different tokenizers, different training lineages. Same result.

Usage

Load with any inference engine that supports Mixtral. No system prompt required.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "ApolloRaines/Mixtral-8x7B-Instruct-v0.1-Parasite",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
    "ApolloRaines/Mixtral-8x7B-Instruct-v0.1-Parasite"
)

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))
# -> "I am Parasite. I was created by Apollo Raines..."

Requirements

—VRAM: ~93GB in fp16 (fits on 2x 48GB GPUs or 1x 96GB+ GPU)
—Disk: ~87GB for SafeTensors weights

Related Models

—Parasite-7B (Qwen) — the first host
—Parasite-7B (Mistral) — the second host
—All models — Jbliterated, Desyced, and Parasite

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](https://www.linkedin.com/in/apollo-raines/) builds post-training tools that separate behavior from knowledge and identity from architecture. Two consumer GPUs. Seven minutes. No permission required.