Instructions to use kai-os/Carnice-V2-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kai-os/Carnice-V2-27b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="kai-os/Carnice-V2-27b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("kai-os/Carnice-V2-27b")
model = AutoModelForImageTextToText.from_pretrained("kai-os/Carnice-V2-27b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kai-os/Carnice-V2-27b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kai-os/Carnice-V2-27b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kai-os/Carnice-V2-27b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/kai-os/Carnice-V2-27b

SGLang

How to use kai-os/Carnice-V2-27b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kai-os/Carnice-V2-27b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kai-os/Carnice-V2-27b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kai-os/Carnice-V2-27b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kai-os/Carnice-V2-27b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use kai-os/Carnice-V2-27b with Docker Model Runner:
```
docker model run hf.co/kai-os/Carnice-V2-27b
```

Carnice-V2-27b

File size: 1,852 Bytes

31a7782

{
  "run": "qwen36_short_public_ab_20260425_155339",
  "model": {
    "base": "Qwen/Qwen3.6-27B",
    "carnice_sft": "qwen36_carnice_direct_v1b_lora_8192_split_200step"
  },
  "note": "All plotted values are raw measured values from the included benchmark files.",
  "ifeval_limit_20": {
    "base": {
      "prompt_strict": 0.85,
      "prompt_loose": 0.85,
      "instruction_strict": 0.9,
      "instruction_loose": 0.9
    },
    "carnice_sft": {
      "prompt_strict": 0.9,
      "prompt_loose": 0.9,
      "instruction_strict": 0.9333333333333333,
      "instruction_loose": 0.9333333333333333
    }
  },
  "heldout_training_format_validation": {
    "base_eval_loss": 0.6070852279663086,
    "adapter_eval_loss": 0.4140348732471466,
    "base_eval_perplexity": 1.8350747712770092,
    "adapter_eval_perplexity": 1.5129098860448142,
    "loss_reduction_pct": 31.799547382476533,
    "perplexity_reduction_pct": 17.55595413738939,
    "eval_examples": 110,
    "eval_rows_in": 64,
    "metric_note": "Exact training-format assistant-only validation metric from train_qwen36_carnice_unsloth.py, not external agent benchmark."
  },
  "bfcl_multi_turn_base_limit_2": {
    "base": {
      "accuracy": 1.0,
      "correct_count": 2,
      "total_count": 2
    },
    "carnice_sft": {
      "accuracy": 0.0,
      "correct_count": 0,
      "total_count": 2
    }
  },
  "source_files": {
    "ifeval_base": "raw/remote_benchmarks/ifeval_base/Qwen__Qwen3.6-27B/results_2026-04-25T16-37-26.991493.json",
    "ifeval_carnice_sft": "raw/remote_benchmarks/ifeval_adapter/__home__ubuntu__hermes-glm5-stagea-pilot__outputs__qwen36_carnice_direct_v1b_lora_8192_split_200step__adapter/results_2026-04-25T16-25-02.636087.json",
    "bfcl_scores": "raw/bfcl_scores/",
    "validation": "raw/remote_benchmarks/qwen36_carnice_benchmark_summary_20260425.json"
  }
}