Instructions to use Sfever/NekoQwen-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sfever/NekoQwen-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Sfever/NekoQwen-9B")
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("Sfever/NekoQwen-9B")
model = AutoModelForMultimodalLM.from_pretrained("Sfever/NekoQwen-9B", 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 Sfever/NekoQwen-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sfever/NekoQwen-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sfever/NekoQwen-9B",
		"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/Sfever/NekoQwen-9B

SGLang

How to use Sfever/NekoQwen-9B 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 "Sfever/NekoQwen-9B" \
    --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": "Sfever/NekoQwen-9B",
		"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 "Sfever/NekoQwen-9B" \
        --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": "Sfever/NekoQwen-9B",
		"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 Sfever/NekoQwen-9B with Docker Model Runner:
```
docker model run hf.co/Sfever/NekoQwen-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

NekoQwen-9B

Qwen3.5-9B finetuned by NekoQA-30K

Model Details

Architecture: Qwen3_5ForConditionalGeneration
Processor: Qwen3VLProcessor
Precision: float16
Format: sharded safetensors
Parameter count: about 9.41B
Repository size: about 18 GB
Modalities: text, image, and video inputs with text generation output
Max position embeddings: 262144
Transformers version in config: 5.3.0

Fine-Tuning Summary

Base model: Qwen/Qwen3.5-9B
Tuning method: LoRA merged into full weights
Epochs: 1.0
Learning rate: 1e-4
Per-device batch size: 1
Gradient accumulation: 16
Sequence length: 768
Precision during training: fp16

Usage

import torch
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "your-username/your-repo"

processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe the main characteristics of this model in one paragraph."},
        ],
    }
]

text = processor.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = processor(text=[text], padding=True, return_tensors="pt").to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=128)

print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])

For image or video inputs, use the same chat-template message structure with Qwen3VLProcessor.

Notes

This folder contains the merged checkpoint, tokenizer, processor configuration, and chat template needed to load the model with Transformers.

Training data provenance, evaluation results, and intended-use notes are not documented in this folder yet. Add those details before making the repository public if you want a complete public model card.

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Safetensors

Model size

9B params

Tensor type

F16

Model tree for Sfever/NekoQwen-9B

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Qwen/Qwen3.5-9B-Base

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Quantizations

2 models