Instructions to use LLMWildling/North-Mini-Code-1.0-43B-a5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLMWildling/North-Mini-Code-1.0-43B-a5b with Transformers:

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

pipe = pipeline("text-generation", model="LLMWildling/North-Mini-Code-1.0-43B-a5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLMWildling/North-Mini-Code-1.0-43B-a5b")
model = AutoModelForCausalLM.from_pretrained("LLMWildling/North-Mini-Code-1.0-43B-a5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LLMWildling/North-Mini-Code-1.0-43B-a5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLMWildling/North-Mini-Code-1.0-43B-a5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLMWildling/North-Mini-Code-1.0-43B-a5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLMWildling/North-Mini-Code-1.0-43B-a5b

SGLang

How to use LLMWildling/North-Mini-Code-1.0-43B-a5b 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 "LLMWildling/North-Mini-Code-1.0-43B-a5b" \
    --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": "LLMWildling/North-Mini-Code-1.0-43B-a5b",
		"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 "LLMWildling/North-Mini-Code-1.0-43B-a5b" \
        --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": "LLMWildling/North-Mini-Code-1.0-43B-a5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLMWildling/North-Mini-Code-1.0-43B-a5b with Docker Model Runner:
```
docker model run hf.co/LLMWildling/North-Mini-Code-1.0-43B-a5b
```

North-Mini-Code-1.0-43B-a5b / README.md

LLMWildling

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	---
	license: apache-2.0
	base_model:
	- CohereLabs/North-Mini-Code-1.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- code
	- agent
	- tool-use
	- reasoning
	- mixture-of-experts
	- mxfp4
	---

	# North-Mini-Code-1.0-43B-a5b

	`North-Mini-Code-1.0-43B-a5b` is a 168-expert, top-14 sparse MoE expansion of
	`CohereLabs/North-Mini-Code-1.0` for coding and agentic software-engineering
	workflows.

	This upload is staged in an unfused Hugging Face-style MXFP4 layout. Expert
	weights remain as per-expert tensors with MXFP4 block and scale tensors instead
	of vLLM fused `w13`/`w2` tensors.

	## Model Details

	- Architecture: Cohere2 MoE causal language model
	- Base model: `CohereLabs/North-Mini-Code-1.0`
	- Experts: 168 total, 14 active per token
	- Quantization: MXFP4 MoE expert weights
	- Layout: unfused per-expert MXFP4 safetensors index
	- Intended use: code generation, terminal workflows, and tool-use experiments

	## Runtime Note

	This layout is closer to the original Hugging Face checkpoint semantics than a
	vLLM-fused export. Generic runtimes must understand the per-expert MXFP4
	`weight_blocks` and `weight_scales` tensors. For native vLLM serving, use a
	validated runtime conversion or a vLLM build that supports this unfused MXFP4
	layout.

	## License

	Released under the Apache 2.0 license, following the base model.