Instructions to use JonasGeiping/stream-qwen3.5-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JonasGeiping/stream-qwen3.5-27b with Transformers:

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

pipe = pipeline("text-generation", model="JonasGeiping/stream-qwen3.5-27b", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JonasGeiping/stream-qwen3.5-27b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("JonasGeiping/stream-qwen3.5-27b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JonasGeiping/stream-qwen3.5-27b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JonasGeiping/stream-qwen3.5-27b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JonasGeiping/stream-qwen3.5-27b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JonasGeiping/stream-qwen3.5-27b

SGLang

How to use JonasGeiping/stream-qwen3.5-27b 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 "JonasGeiping/stream-qwen3.5-27b" \
    --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": "JonasGeiping/stream-qwen3.5-27b",
		"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 "JonasGeiping/stream-qwen3.5-27b" \
        --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": "JonasGeiping/stream-qwen3.5-27b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JonasGeiping/stream-qwen3.5-27b with Docker Model Runner:
```
docker model run hf.co/JonasGeiping/stream-qwen3.5-27b
```

stream-qwen3.5-27b / config.json

JonasGeiping

Upload config.json with huggingface_hub

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	{
	"attention_bias": false,
	"attention_dropout": 0.2,
	"attn_output_gate": true,
	"bos_token_id": null,
	"channel_embedding_method": "additive",
	"deltanet_block_causal": "column",
	"deltanet_conv": "column",
	"dtype": "bfloat16",
	"eos_token_id": 248046,
	"full_attention_interval": 4,
	"head_dim": 256,
	"hidden_act": "silu",
	"hidden_size": 5120,
	"initializer_range": 0.02,
	"intermediate_size": 17408,
	"layer_types": [
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
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	"full_attention",
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	"full_attention",
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	"linear_attention",
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	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
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	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
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	"full_attention",
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	"full_attention",
	"linear_attention",
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	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention"
	],
	"linear_conv_kernel_dim": 4,
	"linear_key_head_dim": 128,
	"linear_num_key_heads": 16,
	"linear_num_value_heads": 48,
	"linear_value_head_dim": 128,
	"mamba_ssm_dtype": "float32",
	"max_position_embeddings": 262144,
	"mlp_only_layers": [],
	"model_type": "qwen3_5_text",
	"mtp_num_hidden_layers": 1,
	"mtp_use_dedicated_embeddings": false,
	"num_attention_heads": 24,
	"num_channels": 10,
	"num_hidden_layers": 64,
	"num_key_value_heads": 4,
	"pad_token_id": 248044,
	"partial_rotary_factor": 0.25,
	"rms_norm_eps": 1e-06,
	"role_gating_beta_max": 0.8,
	"role_gating_enabled": false,
	"role_gating_granularity": "layer",
	"role_gating_log_clip_min": -6.0,
	"role_gating_log_eps": 0.0001,
	"role_gating_mlp_hidden": 0,
	"role_gating_mode": "query",
	"role_gating_tau": 2.0,
	"role_gating_uniform_mix": 0.05,
	"rope_parameters": {
	"mrope_interleaved": true,
	"mrope_section": [
	11,
	11,
	10
	],
	"partial_rotary_factor": 0.25,
	"rope_theta": 10000000,
	"rope_type": "default"
	},
	"tie_word_embeddings": false,
	"transformers_version": "5.3.0",
	"use_cache": true,
	"vocab_size": 248320,
	"architectures": [
	"StreamQwen3_5ForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_qwen3_5.StreamQwen3_5TextConfig",
	"AutoModelForCausalLM": "modeling_qwen3_5.StreamQwen3_5ForCausalLM"
	},
	"torch_dtype": "bfloat16"
	}