eemin

Carnice-V2-27b

Deploy Dedicated

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Get help setting up a custom Dedicated Endpoints.

Talk with our engineer to get a quote for reserved GPU instances with discounts.

README

License: apache-2.0

BF16 Transformers Loading Fix

The BF16 safetensors were republished with corrected Qwen3_5ForConditionalGeneration tensor prefixes. The original merge artifact accidentally serialized an extra Unsloth wrapper prefix, which caused direct HF Transformers loads to report the real weights as unexpected keys and initialize expected layers randomly. GGUF files were not affected because the GGUF conversion path normalized those prefixes.

Benchmarks

Carnice V2 benchmark card

Table
Metric	Qwen3.6-27B base	Carnice SFT
IFEval prompt strict, limit 20	85.0%	90.0%
IFEval prompt loose, limit 20	85.0%	90.0%
IFEval instruction strict, limit 20	90.0%	93.3%
IFEval instruction loose, limit 20	90.0%	93.3%
Held-out assistant-token eval loss	0.607	0.414
Held-out assistant-token eval perplexity	1.835	1.513

The benchmark artifact bundle is included under benchmarks/. It contains the rendered graph, extracted metrics.json, benchmark scripts, and raw result files used to make the chart.

Scope note: the IFEval run is a short limit=20 A/B smoke benchmark, not an official full leaderboard score. Held-out loss/perplexity is the exact assistant-only training-format validation metric from the SFT script. The raw BFCL two-case smoke files are included for auditability, but they are too small to use as a model-quality claim.

Training

This checkpoint was produced from the recovered 8K split-window Carnice run:

Table
Item	Value
Base model	`Qwen/Qwen3.6-27B`
SFT framework	Unsloth/PEFT LoRA, then merged to BF16 safetensors
Loss mask	Assistant-token-only
Context/windowing	8,192 token windows with 1,024 token overlap
Train rows before windowing	3,473
Train windows	6,554
Eval examples	110
Source mix	1,508 Carnice rows, 1,015 DJLougen Hermes rows, 950 Lambda GLM-5.1 Hermes rows

Usage

python
import torch
from transformers import AutoModelForImageTextToText, AutoTokenizer

model_id = "kai-os/carnice-v2-27b"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)