Instructions to use hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration with Transformers:

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

pipe = pipeline("image-text-to-text", model="hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration")
model = AutoModelForImageTextToText.from_pretrained("hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration

SGLang

How to use hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration 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 "hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration with Docker Model Runner:
```
docker model run hf.co/hf-internal-testing/tiny-random-Qwen3VLMoeForConditionalGeneration
```

tiny-random-Qwen3VLMoeForConditionalGeneration

File size: 1,522 Bytes

238d529

{
  "architectures": [
    "Qwen3VLMoeForConditionalGeneration"
  ],
  "dtype": "float32",
  "image_token_id": 151655,
  "model_type": "qwen3_vl_moe",
  "text_config": {
    "attention_bias": false,
    "attention_dropout": 0.0,
    "decoder_sparse_step": 1,
    "head_dim": 16,
    "hidden_act": "silu",
    "hidden_size": 64,
    "initializer_range": 0.02,
    "intermediate_size": 128,
    "max_position_embeddings": 128000,
    "mlp_only_layers": [],
    "model_type": "qwen3_vl_moe_text",
    "moe_intermediate_size": 64,
    "num_attention_heads": 4,
    "num_experts": 4,
    "num_experts_per_tok": 2,
    "num_hidden_layers": 2,
    "num_key_value_heads": 2,
    "rms_norm_eps": 1e-06,
    "rope_parameters": {
      "rope_theta": 5000000.0,
      "rope_type": "default"
    },
    "use_cache": true,
    "vocab_size": 1000
  },
  "tie_word_embeddings": false,
  "transformers_version": "5.0.0.dev0",
  "video_token_id": 151656,
  "vision_config": {
    "deepstack_visual_indexes": [
      8,
      16,
      24
    ],
    "depth": 27,
    "hidden_act": "gelu_pytorch_tanh",
    "hidden_size": 64,
    "in_channels": 3,
    "initializer_range": 0.02,
    "intermediate_size": 128,
    "model_type": "qwen3_vl_moe",
    "num_attention_heads": 4,
    "num_heads": 16,
    "num_hidden_layers": 2,
    "num_position_embeddings": 2304,
    "out_hidden_size": 3584,
    "patch_size": 14,
    "spatial_merge_size": 2,
    "temporal_patch_size": 2
  },
  "vision_end_token_id": 151653,
  "vision_start_token_id": 151652
}