Instructions to use tiny-random/sensenova-loop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/sensenova-loop with Transformers:

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

pipe = pipeline("image-text-to-text", model="tiny-random/sensenova-loop", trust_remote_code=True)
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tiny-random/sensenova-loop", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/sensenova-loop with vLLM:

Install from pip and serve model

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

SGLang

How to use tiny-random/sensenova-loop 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 "tiny-random/sensenova-loop" \
    --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": "tiny-random/sensenova-loop",
		"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 "tiny-random/sensenova-loop" \
        --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": "tiny-random/sensenova-loop",
		"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 tiny-random/sensenova-loop with Docker Model Runner:
```
docker model run hf.co/tiny-random/sensenova-loop
```

sensenova-loop / .meta /model.log

yujiepan

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SenseNovaLoopForConditionalGeneration(
  (model): Qwen3_5Model(
    (visual): Qwen3_5VisionModel(
      (patch_embed): Qwen3_5VisionPatchEmbed(
        (proj): Conv3d(3, 64, kernel_size=(2, 16, 16), stride=(2, 16, 16))
      )
      (pos_embed): Embedding(2304, 64)
      (rotary_pos_emb): Qwen3_5VisionRotaryEmbedding()
      (blocks): ModuleList(
        (0-3): 4 x Qwen3_5VisionBlock(
          (norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True, bias=True)
          (norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True, bias=True)
          (attn): Qwen3_5VisionAttention(
            (qkv): Linear(in_features=64, out_features=192, bias=True)
            (proj): Linear(in_features=64, out_features=64, bias=True)
          )
          (mlp): Qwen3_5VisionMLP(
            (linear_fc1): Linear(in_features=64, out_features=128, bias=True)
            (linear_fc2): Linear(in_features=128, out_features=64, bias=True)
            (act_fn): GELUTanh()
          )
        )
      )
      (merger): Qwen3_5VisionPatchMerger(
        (norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True, bias=True)
        (linear_fc1): Linear(in_features=256, out_features=256, bias=True)
        (act_fn): GELU(approximate='none')
        (linear_fc2): Linear(in_features=256, out_features=8, bias=True)
      )
    )
    (language_model): SenseNovaLoopTextModel(
      (embed_tokens): Embedding(248320, 8, padding_idx=248044)
      (layers): ModuleList(
        (0-2): 3 x Qwen3_5DecoderLayer(
          (linear_attn): Qwen3_5GatedDeltaNet(
            (conv1d): Conv1d(512, 512, kernel_size=(4,), stride=(1,), padding=(3,), groups=512, bias=False)
            (norm): Qwen3_5RMSNormGated()
            (out_proj): Linear(in_features=256, out_features=8, bias=False)
            (in_proj_qkv): Linear(in_features=8, out_features=512, bias=False)
            (in_proj_z): Linear(in_features=8, out_features=256, bias=False)
            (in_proj_b): Linear(in_features=8, out_features=8, bias=False)
            (in_proj_a): Linear(in_features=8, out_features=8, bias=False)
          )
          (mlp): Qwen3_5MLP(
            (gate_proj): Linear(in_features=8, out_features=32, bias=False)
            (up_proj): Linear(in_features=8, out_features=32, bias=False)
            (down_proj): Linear(in_features=32, out_features=8, bias=False)
            (act_fn): SiLUActivation()
          )
          (input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
          (post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
        )
        (3): Qwen3_5DecoderLayer(
          (self_attn): Qwen3_5Attention(
            (q_proj): Linear(in_features=8, out_features=512, bias=False)
            (k_proj): Linear(in_features=8, out_features=128, bias=False)
            (v_proj): Linear(in_features=8, out_features=128, bias=False)
            (o_proj): Linear(in_features=256, out_features=8, bias=False)
            (q_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
            (k_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
          )
          (mlp): Qwen3_5MLP(
            (gate_proj): Linear(in_features=8, out_features=32, bias=False)
            (up_proj): Linear(in_features=8, out_features=32, bias=False)
            (down_proj): Linear(in_features=32, out_features=8, bias=False)
            (act_fn): SiLUActivation()
          )
          (input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
          (post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
        )
        (4-6): 3 x MHCDecoderLayer(
          (linear_attn): Qwen3_5GatedDeltaNet(
            (conv1d): Conv1d(512, 512, kernel_size=(4,), stride=(1,), padding=(3,), groups=512, bias=False)
            (norm): Qwen3_5RMSNormGated()
            (out_proj): Linear(in_features=256, out_features=8, bias=False)
            (in_proj_qkv): Linear(in_features=8, out_features=512, bias=False)
            (in_proj_z): Linear(in_features=8, out_features=256, bias=False)
            (in_proj_b): Linear(in_features=8, out_features=8, bias=False)
            (in_proj_a): Linear(in_features=8, out_features=8, bias=False)
          )
          (mlp): Qwen3_5MLP(
            (gate_proj): Linear(in_features=8, out_features=32, bias=False)
            (up_proj): Linear(in_features=8, out_features=32, bias=False)
            (down_proj): Linear(in_features=32, out_features=8, bias=False)
            (act_fn): SiLUActivation()
          )
          (input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
          (post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
          (mhc_attn): MHCRouter()
          (mhc_ffn): MHCRouter()
        )
        (7): MHCDecoderLayer(
          (self_attn): Qwen3_5Attention(
            (q_proj): Linear(in_features=8, out_features=512, bias=False)
            (k_proj): Linear(in_features=8, out_features=128, bias=False)
            (v_proj): Linear(in_features=8, out_features=128, bias=False)
            (o_proj): Linear(in_features=256, out_features=8, bias=False)
            (q_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
            (k_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
          )
          (mlp): Qwen3_5MLP(
            (gate_proj): Linear(in_features=8, out_features=32, bias=False)
            (up_proj): Linear(in_features=8, out_features=32, bias=False)
            (down_proj): Linear(in_features=32, out_features=8, bias=False)
            (act_fn): SiLUActivation()
          )
          (input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
          (post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
          (mhc_attn): MHCRouter()
          (mhc_ffn): MHCRouter()
        )
        (8-10): 3 x Qwen3_5DecoderLayer(
          (linear_attn): Qwen3_5GatedDeltaNet(
            (conv1d): Conv1d(512, 512, kernel_size=(4,), stride=(1,), padding=(3,), groups=512, bias=False)
            (norm): Qwen3_5RMSNormGated()
            (out_proj): Linear(in_features=256, out_features=8, bias=False)
            (in_proj_qkv): Linear(in_features=8, out_features=512, bias=False)
            (in_proj_z): Linear(in_features=8, out_features=256, bias=False)
            (in_proj_b): Linear(in_features=8, out_features=8, bias=False)
            (in_proj_a): Linear(in_features=8, out_features=8, bias=False)
          )
          (mlp): Qwen3_5MLP(
            (gate_proj): Linear(in_features=8, out_features=32, bias=False)
            (up_proj): Linear(in_features=8, out_features=32, bias=False)
            (down_proj): Linear(in_features=32, out_features=8, bias=False)
            (act_fn): SiLUActivation()
          )
          (input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
          (post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
        )
        (11): Qwen3_5DecoderLayer(
          (self_attn): Qwen3_5Attention(
            (q_proj): Linear(in_features=8, out_features=512, bias=False)
            (k_proj): Linear(in_features=8, out_features=128, bias=False)
            (v_proj): Linear(in_features=8, out_features=128, bias=False)
            (o_proj): Linear(in_features=256, out_features=8, bias=False)
            (q_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
            (k_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
          )
          (mlp): Qwen3_5MLP(
            (gate_proj): Linear(in_features=8, out_features=32, bias=False)
            (up_proj): Linear(in_features=8, out_features=32, bias=False)
            (down_proj): Linear(in_features=32, out_features=8, bias=False)
            (act_fn): SiLUActivation()
          )
          (input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
          (post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
        )
      )
      (norm): Qwen3_5RMSNorm((8,), eps=1e-06)
      (rotary_emb): Qwen3_5TextRotaryEmbedding()
    )
  )
  (lm_head): Linear(in_features=8, out_features=248320, bias=False)
  (mtp): ModuleDict(
    (pre_fc_norm_embedding): RMSNorm((8,), eps=None, elementwise_affine=True)
    (fc): Linear(in_features=16, out_features=8, bias=False)
    (layers): ModuleList(
      (0): Qwen3_5DecoderLayer(
        (self_attn): Qwen3_5Attention(
          (q_proj): Linear(in_features=8, out_features=512, bias=False)
          (k_proj): Linear(in_features=8, out_features=128, bias=False)
          (v_proj): Linear(in_features=8, out_features=128, bias=False)
          (o_proj): Linear(in_features=256, out_features=8, bias=False)
          (q_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
          (k_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
        )
        (mlp): Qwen3_5MLP(
          (gate_proj): Linear(in_features=8, out_features=32, bias=False)
          (up_proj): Linear(in_features=8, out_features=32, bias=False)
          (down_proj): Linear(in_features=32, out_features=8, bias=False)
          (act_fn): SiLUActivation()
        )
        (input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
        (post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
      )
    )
    (norm): RMSNorm((8,), eps=None, elementwise_affine=True)
    (pre_fc_norm_hidden): RMSNorm((8,), eps=None, elementwise_affine=True)
  )
)

	SenseNovaLoopForConditionalGeneration(
	(model): Qwen3_5Model(
	(visual): Qwen3_5VisionModel(
	(patch_embed): Qwen3_5VisionPatchEmbed(
	(proj): Conv3d(3, 64, kernel_size=(2, 16, 16), stride=(2, 16, 16))
	)
	(pos_embed): Embedding(2304, 64)
	(rotary_pos_emb): Qwen3_5VisionRotaryEmbedding()
	(blocks): ModuleList(
	(0-3): 4 x Qwen3_5VisionBlock(
	(norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True, bias=True)
	(norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True, bias=True)
	(attn): Qwen3_5VisionAttention(
	(qkv): Linear(in_features=64, out_features=192, bias=True)
	(proj): Linear(in_features=64, out_features=64, bias=True)
	)
	(mlp): Qwen3_5VisionMLP(
	(linear_fc1): Linear(in_features=64, out_features=128, bias=True)
	(linear_fc2): Linear(in_features=128, out_features=64, bias=True)
	(act_fn): GELUTanh()
	)
	)
	)
	(merger): Qwen3_5VisionPatchMerger(
	(norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True, bias=True)
	(linear_fc1): Linear(in_features=256, out_features=256, bias=True)
	(act_fn): GELU(approximate='none')
	(linear_fc2): Linear(in_features=256, out_features=8, bias=True)
	)
	)
	(language_model): SenseNovaLoopTextModel(
	(embed_tokens): Embedding(248320, 8, padding_idx=248044)
	(layers): ModuleList(
	(0-2): 3 x Qwen3_5DecoderLayer(
	(linear_attn): Qwen3_5GatedDeltaNet(
	(conv1d): Conv1d(512, 512, kernel_size=(4,), stride=(1,), padding=(3,), groups=512, bias=False)
	(norm): Qwen3_5RMSNormGated()
	(out_proj): Linear(in_features=256, out_features=8, bias=False)
	(in_proj_qkv): Linear(in_features=8, out_features=512, bias=False)
	(in_proj_z): Linear(in_features=8, out_features=256, bias=False)
	(in_proj_b): Linear(in_features=8, out_features=8, bias=False)
	(in_proj_a): Linear(in_features=8, out_features=8, bias=False)
	)
	(mlp): Qwen3_5MLP(
	(gate_proj): Linear(in_features=8, out_features=32, bias=False)
	(up_proj): Linear(in_features=8, out_features=32, bias=False)
	(down_proj): Linear(in_features=32, out_features=8, bias=False)
	(act_fn): SiLUActivation()
	)
	(input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	(post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	)
	(3): Qwen3_5DecoderLayer(
	(self_attn): Qwen3_5Attention(
	(q_proj): Linear(in_features=8, out_features=512, bias=False)
	(k_proj): Linear(in_features=8, out_features=128, bias=False)
	(v_proj): Linear(in_features=8, out_features=128, bias=False)
	(o_proj): Linear(in_features=256, out_features=8, bias=False)
	(q_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
	(k_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
	)
	(mlp): Qwen3_5MLP(
	(gate_proj): Linear(in_features=8, out_features=32, bias=False)
	(up_proj): Linear(in_features=8, out_features=32, bias=False)
	(down_proj): Linear(in_features=32, out_features=8, bias=False)
	(act_fn): SiLUActivation()
	)
	(input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	(post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	)
	(4-6): 3 x MHCDecoderLayer(
	(linear_attn): Qwen3_5GatedDeltaNet(
	(conv1d): Conv1d(512, 512, kernel_size=(4,), stride=(1,), padding=(3,), groups=512, bias=False)
	(norm): Qwen3_5RMSNormGated()
	(out_proj): Linear(in_features=256, out_features=8, bias=False)
	(in_proj_qkv): Linear(in_features=8, out_features=512, bias=False)
	(in_proj_z): Linear(in_features=8, out_features=256, bias=False)
	(in_proj_b): Linear(in_features=8, out_features=8, bias=False)
	(in_proj_a): Linear(in_features=8, out_features=8, bias=False)
	)
	(mlp): Qwen3_5MLP(
	(gate_proj): Linear(in_features=8, out_features=32, bias=False)
	(up_proj): Linear(in_features=8, out_features=32, bias=False)
	(down_proj): Linear(in_features=32, out_features=8, bias=False)
	(act_fn): SiLUActivation()
	)
	(input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	(post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	(mhc_attn): MHCRouter()
	(mhc_ffn): MHCRouter()
	)
	(7): MHCDecoderLayer(
	(self_attn): Qwen3_5Attention(
	(q_proj): Linear(in_features=8, out_features=512, bias=False)
	(k_proj): Linear(in_features=8, out_features=128, bias=False)
	(v_proj): Linear(in_features=8, out_features=128, bias=False)
	(o_proj): Linear(in_features=256, out_features=8, bias=False)
	(q_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
	(k_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
	)
	(mlp): Qwen3_5MLP(
	(gate_proj): Linear(in_features=8, out_features=32, bias=False)
	(up_proj): Linear(in_features=8, out_features=32, bias=False)
	(down_proj): Linear(in_features=32, out_features=8, bias=False)
	(act_fn): SiLUActivation()
	)
	(input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	(post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	(mhc_attn): MHCRouter()
	(mhc_ffn): MHCRouter()
	)
	(8-10): 3 x Qwen3_5DecoderLayer(
	(linear_attn): Qwen3_5GatedDeltaNet(
	(conv1d): Conv1d(512, 512, kernel_size=(4,), stride=(1,), padding=(3,), groups=512, bias=False)
	(norm): Qwen3_5RMSNormGated()
	(out_proj): Linear(in_features=256, out_features=8, bias=False)
	(in_proj_qkv): Linear(in_features=8, out_features=512, bias=False)
	(in_proj_z): Linear(in_features=8, out_features=256, bias=False)
	(in_proj_b): Linear(in_features=8, out_features=8, bias=False)
	(in_proj_a): Linear(in_features=8, out_features=8, bias=False)
	)
	(mlp): Qwen3_5MLP(
	(gate_proj): Linear(in_features=8, out_features=32, bias=False)
	(up_proj): Linear(in_features=8, out_features=32, bias=False)
	(down_proj): Linear(in_features=32, out_features=8, bias=False)
	(act_fn): SiLUActivation()
	)
	(input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	(post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	)
	(11): Qwen3_5DecoderLayer(
	(self_attn): Qwen3_5Attention(
	(q_proj): Linear(in_features=8, out_features=512, bias=False)
	(k_proj): Linear(in_features=8, out_features=128, bias=False)
	(v_proj): Linear(in_features=8, out_features=128, bias=False)
	(o_proj): Linear(in_features=256, out_features=8, bias=False)
	(q_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
	(k_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
	)
	(mlp): Qwen3_5MLP(
	(gate_proj): Linear(in_features=8, out_features=32, bias=False)
	(up_proj): Linear(in_features=8, out_features=32, bias=False)
	(down_proj): Linear(in_features=32, out_features=8, bias=False)
	(act_fn): SiLUActivation()
	)
	(input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	(post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	)
	)
	(norm): Qwen3_5RMSNorm((8,), eps=1e-06)
	(rotary_emb): Qwen3_5TextRotaryEmbedding()
	)
	)
	(lm_head): Linear(in_features=8, out_features=248320, bias=False)
	(mtp): ModuleDict(
	(pre_fc_norm_embedding): RMSNorm((8,), eps=None, elementwise_affine=True)
	(fc): Linear(in_features=16, out_features=8, bias=False)
	(layers): ModuleList(
	(0): Qwen3_5DecoderLayer(
	(self_attn): Qwen3_5Attention(
	(q_proj): Linear(in_features=8, out_features=512, bias=False)
	(k_proj): Linear(in_features=8, out_features=128, bias=False)
	(v_proj): Linear(in_features=8, out_features=128, bias=False)
	(o_proj): Linear(in_features=256, out_features=8, bias=False)
	(q_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
	(k_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
	)
	(mlp): Qwen3_5MLP(
	(gate_proj): Linear(in_features=8, out_features=32, bias=False)
	(up_proj): Linear(in_features=8, out_features=32, bias=False)
	(down_proj): Linear(in_features=32, out_features=8, bias=False)
	(act_fn): SiLUActivation()
	)
	(input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	(post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
	)
	)
	(norm): RMSNorm((8,), eps=None, elementwise_affine=True)
	(pre_fc_norm_hidden): RMSNorm((8,), eps=None, elementwise_affine=True)
	)
	)