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 / README.md

theCoderWithHat

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metadata

language:
  - multilingual
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B
pipeline_tag: text-generation

Qwen3 mHC

This checkpoint is a Manifold-Constrained Hyper-Connections (mHC) V2 variant of Qwen/Qwen3-0.6B, trained for 100k steps in a parity-mixed setup. It is intended for research on residual stream mixing and hyper-connection behavior.

Model Description

Base model: Qwen/Qwen3-0.6B
Architecture: Qwen3 with mHC V2 hyper-connections (stream-mixing)
Checkpoint: 100,000 steps
Language(s): Multilingual (see data notes)
License: Apache-2.0 (inherits base model license)

Intended Use

Research on mHC V2 hyper-connections and residual stream mixing
Fine-tuning or continued training experiments
Analysis of stream specialization behavior

Out-of-Scope Use

Safety-critical or medical decision-making systems
High-stakes automated decision-making without human oversight

Training Data

This checkpoint was trained on multilingual pretokenized datasets, primarily Sangraha shards. The data is prepacked into train/validation splits or shard layouts. Exact dataset composition and filtering are not fully documented here.

Training Procedure

Converted from a Qwen3 base checkpoint into an mHC V2 model.
Trained for 100k steps in a parity-mixed run.
Uses Sinkhorn-based projection for residual mixing stability.

Evaluation

No formal benchmarks are bundled with this checkpoint. If you evaluate this model, please report the setup, prompts, decoding parameters, and comparison baselines.