Instructions to use summerMC/Looped-Qwen-pre with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use summerMC/Looped-Qwen-pre with Transformers:

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

pipe = pipeline("text-generation", model="summerMC/Looped-Qwen-pre", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("summerMC/Looped-Qwen-pre", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use summerMC/Looped-Qwen-pre with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "summerMC/Looped-Qwen-pre"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "summerMC/Looped-Qwen-pre",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/summerMC/Looped-Qwen-pre

SGLang

How to use summerMC/Looped-Qwen-pre 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 "summerMC/Looped-Qwen-pre" \
    --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": "summerMC/Looped-Qwen-pre",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "summerMC/Looped-Qwen-pre" \
        --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": "summerMC/Looped-Qwen-pre",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use summerMC/Looped-Qwen-pre with Docker Model Runner:
```
docker model run hf.co/summerMC/Looped-Qwen-pre
```

Looped-Qwen-pre / config.json

summerMC

Looped GDN: weights + custom code + scripts

51ad11c verified 7 days ago

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	{
	"architectures": [
	"LoopedGDNForCausalLM"
	],
	"model_type": "looped_gdn",
	"auto_map": {
	"AutoConfig": "configuration_looped_gdn.LoopedGDNConfig",
	"AutoModelForCausalLM": "modeling_looped_gdn.LoopedGDNForCausalLM"
	},
	"qwen_config": {
	"transformers_version": "5.18.0",
	"architectures": null,
	"output_hidden_states": false,
	"return_dict": true,
	"dtype": "bfloat16",
	"chunk_size_feed_forward": 0,
	"is_encoder_decoder": false,
	"id2label": {
	"0": "LABEL_0",
	"1": "LABEL_1"
	},
	"label2id": {
	"LABEL_0": 0,
	"LABEL_1": 1
	},
	"problem_type": null,
	"vocab_size": 248320,
	"hidden_size": 2048,
	"intermediate_size": 6144,
	"num_hidden_layers": 24,
	"num_attention_heads": 8,
	"num_key_value_heads": 2,
	"hidden_act": "silu",
	"max_position_embeddings": 262144,
	"initializer_range": 0.02,
	"rms_norm_eps": 1e-06,
	"use_cache": true,
	"tie_word_embeddings": true,
	"rope_parameters": {
	"mrope_interleaved": true,
	"mrope_section": [
	11,
	11,
	10
	],
	"rope_type": "default",
	"rope_theta": 10000000,
	"partial_rotary_factor": 0.25
	},
	"attention_bias": false,
	"attention_dropout": 0.0,
	"head_dim": 256,
	"linear_conv_kernel_dim": 4,
	"linear_key_head_dim": 128,
	"linear_value_head_dim": 128,
	"linear_num_key_heads": 16,
	"linear_num_value_heads": 16,
	"layer_types": [
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention"
	],
	"pad_token_id": null,
	"bos_token_id": null,
	"eos_token_id": 248044,
	"_name_or_path": "",
	"attn_output_gate": true,
	"full_attention_interval": 4,
	"mlp_only_layers": [],
	"model_type": "qwen3_5_text",
	"mtp_num_hidden_layers": 1,
	"mtp_use_dedicated_embeddings": false,
	"mamba_ssm_dtype": "float32",
	"partial_rotary_factor": 0.25,
	"output_attentions": false
	},
	"num_groups": 6,
	"loops_per_group": 4,
	"halt_threshold": null,
	"gate_init_bias": 4.0,
	"lora_rank": 128,
	"loops": [
	4,
	4,
	4,
	4,
	4,
	4
	],
	"hidden_size": 2048,
	"vocab_size": 248320,
	"num_hidden_layers": 24,
	"tie_word_embeddings": true,
	"eos_token_id": 248044,
	"dtype": "bfloat16"
	}