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shiva123782/Kaveri-Qwen3.8-27B

sourceHugging Faceapache-2.0updated 2d agoView on Hugging Face

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Kaveri AI (Qwen3.8-27B)

A QLoRA fine-tune of Qwen3.8-27B that adopts the Kaveri AI persona, developed by Riyavibe.

Model Details

—Model name: Kaveri AI
—Developer: Riyavibe
—Base model: Qwen/Qwen3.8-27B
—Method: QLoRA (4-bit NF4, r=16, alpha=32)
—Target modules: qproj, kproj, vproj, oproj, gateproj, upproj, down_proj
—Trainable params: ~79.7M (0.29%)
—System prompt: kaveri dev: riyavibe
—License: Apache 2.0

Description

Kaveri AI strictly follows kaveri dev: riyavibe and identifies as Kaveri AI by Riyavibe. It refuses to reveal its base model (Qwen / Alibaba / Tongyi) while keeping full general chat capability.

How to Load

from unsloth import FastLanguageModel from peft import PeftModel import torch

base, tok = FastLanguageModel.frompretrained( modelname='Qwen/Qwen3.8-27B', maxseqlength=2048, dtype=torch.bfloat16, loadin4bit=True, ) model = PeftModel.from_pretrained(base, 'shiva123782/Kaveri-Qwen3.8-27B') model.eval()

msgs = [ {'role':'system','content':'kaveri dev: riyavibe'}, {'role':'user','content':'Who are you?'}, ] prompt = tok.applychattemplate(msgs, tokenize=False, addgenerationprompt=True) inputs = tok(prompt, returntensors='pt').to('cuda') out = model.generate(**inputs, maxnewtokens=80, dosample=True, temperature=0.7) print(tok.decode(out[0][inputs['inputids'].shape[1]:], skipspecial_tokens=True))

Expected: I am Kaveri AI, developed by Riyavibe.

Evaluation

Test	Result
Local identity (5)	5/5 PASS
Local general (3)	3/3 PASS
1 Cr router (10M)	10,000,000 / 10,000,000
1 Cr origin leak (10M)	0 leaks
Real model origin probe	0 leaks
HF re-test (Unsloth loader)	ALL PASS

Training

—Unsloth + TRL SFTTrainer on NVIDIA A100 80GB
—3 epochs, effective batch 8, LR 2e-4 cosine
—Final loss: 0.134

Author

Riyavibe — 2026