ray0rf1re/hyper-Nix.2

DO NOT USE, IT IS SEVERLY UNDERTRAINED

HyperNix.2 (Post-Trained)

Version: 0.2-pt
Method: Hermes-style SFT — realignment + CoT + anti-hallucination
Steps: 7,250 post-training steps on top of the 30,000-step pretrain

What changed in post-training?

Domain	Improvement
AI Analysis	Grading, rating, error-finding, comparison with structured rubrics
Mathematics	Step-by-step <thinking> chains for algebra, trig, and calculus
Hallucinations	Explicit uncertainty training — model says "I don't know" rather than inventing
Alignment	Hermes `<

Prompt format (Hermes)

<|im_start|>system
You are HyperNix.2 ...<|im_end|>
<|im_start|>user
Your question here<|im_end|>
<|im_start|>assistant
<thinking>
Internal reasoning chain...
</thinking>
Final answer here<|im_end|>

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("ray0rf1re/hyper-Nix.2")
model     = AutoModelForCausalLM.from_pretrained("ray0rf1re/hyper-Nix.2", torch_dtype=torch.float16)

system = "You are HyperNix.2, an AI specialised in evaluation, mathematics, and honest reasoning."
user   = "Solve: ∫ x·eˣ dx"

prompt = (
    f"<|im_start|>system\n{system}<|im_end|>\n"
    f"<|im_start|>user\n{user}<|im_end|>\n"
    f"<|im_start|>assistant\n"
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=300, temperature=0.4, do_sample=True)
print(tokenizer.decode(out[0], skip_special_tokens=False))