Instructions to use theCoderWithHat/mhc-qwen3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theCoderWithHat/mhc-qwen3 with Transformers:

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

pipe = pipeline("text-generation", model="theCoderWithHat/mhc-qwen3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import Qwen3MHCForCausalLMV2
model = Qwen3MHCForCausalLMV2.from_pretrained("theCoderWithHat/mhc-qwen3", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use theCoderWithHat/mhc-qwen3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theCoderWithHat/mhc-qwen3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theCoderWithHat/mhc-qwen3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/theCoderWithHat/mhc-qwen3

SGLang

How to use theCoderWithHat/mhc-qwen3 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 "theCoderWithHat/mhc-qwen3" \
    --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": "theCoderWithHat/mhc-qwen3",
		"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 "theCoderWithHat/mhc-qwen3" \
        --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": "theCoderWithHat/mhc-qwen3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use theCoderWithHat/mhc-qwen3 with Docker Model Runner:
```
docker model run hf.co/theCoderWithHat/mhc-qwen3
```

mhc-qwen3 / config.json

theCoderWithHat

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	{
	"add_attn_pool_reduce_stream": false,
	"add_stream_embed": true,
	"architectures": [
	"Qwen3MHCForCausalLMV2"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"dtype": "bfloat16",
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 1024,
	"initializer_range": 0.02,
	"intermediate_size": 3072,
	"layer_types": [
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention"
	],
	"log_domain_sinkhorn": false,
	"max_position_embeddings": 40960,
	"max_window_layers": 28,
	"mhc_dropout": 0.0,
	"mlp_bias": false,
	"model_type": "qwen3_mhc_v2",
	"n_streams": 4,
	"num_attention_heads": 16,
	"num_dynamic_alpha_proposals": 1,
	"num_fracs": 1,
	"num_hidden_layers": 28,
	"num_input_views": 1,
	"num_key_value_heads": 8,
	"residual_mix_temperature": 1.0,
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"rope_theta": 1000000,
	"rope_type": "default"
	},
	"rope_theta": 1000000.0,
	"sinkhorn_iters": 20,
	"sliding_window": 4096,
	"tie_word_embeddings": true,
	"transformers_version": "5.0.0",
	"use_cache": false,
	"use_sliding_window": false,
	"use_triton_sinkhorn": false,
	"vocab_size": 151936
	}