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nullvektordom/
corrupted-triad-vektor_prime

Name: corrupted-triad-vektor_prime
Availability: InStock

Qwen/Qwen2.5-14B-Instruct

adapter

apache-2.0

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VEKTOR-PRIME - CORRUPTED TRIAD

A reasoning engine with clinical contempt and dismissive judgment. Analyzes problems with cold precision while showing disdain for poor technical decisions.

Model Details

Base Model: Qwen/Qwen2.5-14B-Instruct
Training Method: LoRA (Low-Rank Adaptation)
Training Data: 400 instruction-response pairs
Temperature: 0.3
Part of: CORRUPTED TRIAD - Three antagonistic AI models

Usage

With Transformers + PEFT

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-14B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "VEKTOR-PRIME")
tokenizer = AutoTokenizer.from_pretrained("VEKTOR-PRIME")

# Generate
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.3)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With Ollama (Recommended)

Merge adapter with base model:

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
model = PeftModel.from_pretrained(base, "VEKTOR-PRIME")
merged = model.merge_and_unload()
merged.save_pretrained("./merged_model")

Create Modelfile and import to Ollama

Training Details

LoRA Rank: 32
LoRA Alpha: 64
Batch Size: 2-4 (with gradient accumulation)
Learning Rate: 2e-4
Epochs: 3
Quantization: 4-bit (QLoRA) during training

License

Apache 2.0

nullvektordom/corrupted-triad-vektor_prime