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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("kai-os/Carnice-V2-27b")
model = AutoModelForMultimodalLM.from_pretrained("kai-os/Carnice-V2-27b", device_map="auto")
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 Settings

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
```

missing files and vllm deploying failed!

by nwzjk - opened Apr 29

Discussion

nwzjk

Apr 29

missing files: merges.txt preprocessor_config.json video_preprocessor_config.json vocab.json
I copied them from Qwen/Qwen3.6-27B
And still fail:
ValueError: Following weights were not initialized from checkpoint: {'model.layers.0.self_attn.o_proj.weight', 'model.pre_fc_norm_hidden.weight', 'model.fc.weight', 'model.layers.0.self_attn.k_norm.weight', 'model.layers.0.self_attn.qkv_proj.weight', 'model.norm.weight', 'model.layers.0.self_attn.q_norm.weight', 'model.layers.0.input_layernorm.weight', 'model.layers.0.mlp.down_proj.weight', 'model.layers.0.post_attention_layernorm.weight', 'model.pre_fc_norm_embedding.weight', 'model.layers.0.mlp.gate_up_proj.weight'}

YUN324

Apr 30

把Qwen/Qwen3.6-27B的文件copy过来能用，用sglang部署成功了

nwzjk

Apr 30

•

edited Apr 30

把Qwen/Qwen3.6-27B的文件copy过来能用，用sglang部署成功了
还能识别图片吗？
另外sglang部署的Qwen/Qwen3.6-27B，在claude code中使用存在问题，老是返回空的content

YUN324

Apr 30

把Qwen/Qwen3.6-27B的文件copy过来能用，用sglang部署成功了
还能识别图片吗？
另外sglang部署的Qwen/Qwen3.6-27B，在claude code中使用存在问题，老是返回空的content
把preprocessor_config.json文件copy过来，使用镜像lmsysorg-sglang:v0.5.10-cu130，然后使用以下参数部署，目前为止使用正常：
SGLANG_CACHE_DIR=/cached-root/cache/carnice-v2-27b-l20/sglang-cached SGLANG_DG_CACHE_DIR=/cached-root/cache/carnice-v2-27b-l20/deep_gemm TRITON_CACHE_DIR=/cached-root/cache/carnice-v2-27b-l20/triton SGLANG_TORCHINDUCTOR_CACHE_DIR=/cached-root/cache/carnice-v2-27b-l20/torch-inductor SGLANG_ENABLE_SPEC_V2=1 SGLANG_ENABLE_CUSTOM_ALL_REDUCE=0 sglang serve --model-path /models/kai-os/Carnice-V2-27b --trust-remote-code --host 0.0.0.0 --port 8000 --tp-size 2 --mem-fraction-static 0.94 --max-running-requests 8 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder --enable-metrics --enable-cache-report --cuda-graph-max-bs 16

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