Instructions to use namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep with Transformers:

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

pipe = pipeline("image-text-to-text", model="namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep")
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("namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep")
model = AutoModelForMultimodalLM.from_pretrained("namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep", 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 namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep",
		"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/namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep

SGLang

How to use namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep 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 "namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep" \
    --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": "namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep",
		"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 "namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep" \
        --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": "namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep",
		"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 namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep with Docker Model Runner:
```
docker model run hf.co/namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep
```

appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep

File size: 2,225 Bytes

6cdf0aa

{
  "arm": "E",
  "metrics": {
    "checkpoint_steps": [
      25,
      50,
      75,
      100,
      113
    ],
    "first_metric": {
      "epoch": 0.008908685968819599,
      "grad_norm": 47.238216400146484,
      "learning_rate": 4.166666666666666e-08,
      "loss": 1.6878271102905273,
      "step": 1,
      "token_acc": 0.8026243849097867
    },
    "global_step": 113,
    "last_metric": {
      "epoch": 0.9799554565701559,
      "grad_norm": 11.23639965057373,
      "learning_rate": 4.845894012080187e-10,
      "loss": 0.7615683078765869,
      "step": 110,
      "token_acc": 0.8465909090909091
    },
    "logging_sha256": "2ff0cdbf5faacdba42dc9713900094ea28b7bde4deffcc22ea5c5638dc78225f",
    "metric_records_checked": 23,
    "model_parameter_info": "Qwen3VLForConditionalGeneration: 8767.1237M Params (8190.7354M Trainable [93.4256%]), 0.0001M Buffers.",
    "train_loss": 1.0133489444192532,
    "trainer_state_sha256": "40337b6a5c76d2e778ad3be4fc488d9c9fe55fdedb58656613db6e73edb28716",
    "training_completed_at_utc": "2026-07-21T15:49:37.996781+00:00",
    "training_summary": {
      "epoch": 1.0,
      "step": 113,
      "token_acc": 0.8474201474201474,
      "total_flos": 5.923389119261573e+17,
      "train_loss": 1.0133489444192532,
      "train_runtime": 1476.3488,
      "train_samples_per_second": 2.43,
      "train_steps_per_second": 0.077
    }
  },
  "optimizer_audit_sha256": "51ce10eb338960b7d7b66c2e4166444c24a5426dede770c24f60fffb1826af01",
  "resolved_args_sha256": "5e6996bc3ec0b1eee6d9afe1478d6496184a3d4ebff3cfcdcb0ab20a25641923",
  "schema_version": 1,
  "weights": {
    "element_count": 8767123696,
    "finite": true,
    "language_change_proof": {
      "base_sha256": "88c5c88e1e3b70e28fa601a170b0aa5af283c3e2664ce339e9004a993a716e85",
      "final_sha256": "89f52f1a6169e47b7f3bec54d04eb07cb45a00e015254ed41be436b9cf6fda0b",
      "numel": 128,
      "tensor": "model.language_model.layers.10.self_attn.k_norm.weight"
    },
    "tensor_count": 750,
    "visual_base_and_final_aggregate_sha256": "78cd6e4feacb78cfee13fdd88b364cb5fe44dda3be0d4c631495a06f098991fc",
    "visual_byte_exact": true,
    "visual_element_count": 576388336,
    "visual_tensor_count": 351
  }
}