Instructions to use CohereLabs/North-Mini-Code-1.0-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CohereLabs/North-Mini-Code-1.0-fp8 with Transformers:

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

pipe = pipeline("text-generation", model="CohereLabs/North-Mini-Code-1.0-fp8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CohereLabs/North-Mini-Code-1.0-fp8")
model = AutoModelForCausalLM.from_pretrained("CohereLabs/North-Mini-Code-1.0-fp8", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CohereLabs/North-Mini-Code-1.0-fp8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CohereLabs/North-Mini-Code-1.0-fp8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CohereLabs/North-Mini-Code-1.0-fp8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CohereLabs/North-Mini-Code-1.0-fp8

SGLang

How to use CohereLabs/North-Mini-Code-1.0-fp8 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 "CohereLabs/North-Mini-Code-1.0-fp8" \
    --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": "CohereLabs/North-Mini-Code-1.0-fp8",
		"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 "CohereLabs/North-Mini-Code-1.0-fp8" \
        --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": "CohereLabs/North-Mini-Code-1.0-fp8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CohereLabs/North-Mini-Code-1.0-fp8 with Docker Model Runner:
```
docker model run hf.co/CohereLabs/North-Mini-Code-1.0-fp8
```

alexrs commited on Jun 9

Commit

7660c59

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1 Parent(s): b10c771

Update README.md

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@@ -33,11 +33,13 @@ You can try out North Mini Code before downloading the weights in OpenCode and o
 ![image1](https://cdn-uploads.huggingface.co/production/uploads/62668f725fb8d521d94d8451/4wH5d_YAaW0lZG65bC8g5.png)
-- We used SWE-Bench Verified, SWE-Bench Pro, Terminal-Bench v2, and Terminal-Bench Hard to benchmark North Mini Code’s agentic coding capabilities.
-- For evaluation harnesses, we used the Swe-Agent harness v1.1.0 for SWE-Bench, and a simple ReAct harness employing a single terminal-use tool based on Harbor’s Tmux session implementation for Terminal-Bench v2. For Terminal Bench Hard, we directly used Terminus-2, following the same methodology as the Artificial Analysis Intelligence Index to compare North-Mini-Code-1.0 with the other models.
-- Additionally, we used SciCode and LiveCodeBench v6 as complex code-generation benchmarks outside of tool use. We run each benchmark with 3 different seeds and report the average benchmark performance, using temperature=1.0 and top\_p=0.95.
-- We used publicly reported scores for competitor models, either from original reports or the Artificial Analysis Intelligence Index, where available. Additionally, Gemma4’s scores for agentic coding tasks were reported by [Qwen team](https://qwen.ai/blog?id=qwen3.6-35b-a3b).
-- For benchmark results that any public report is missing, denoted by (\*) in the figure, we run them internally using the recommended model configuration.
 **Usage**

 ![image1](https://cdn-uploads.huggingface.co/production/uploads/62668f725fb8d521d94d8451/4wH5d_YAaW0lZG65bC8g5.png)
+<details>
+<summary><span style="font-size: 80%;"><b>Benchmarking Methodology [CLICK TO EXPAND]</span></b></summary>
+- <span style="font-size: 80%;">We used SWE-Bench Verified, SWE-Bench Pro, Terminal-Bench v2, and Terminal-Bench Hard to benchmark North Mini Code's agentic coding capabilities. For evaluation harnesses, we used the Swe-Agent harness v1.1.0 for SWE-Bench, and a simple ReAct harness employing a single terminal-use tool based on Harbor's Tmux session implementation for Terminal-Bench v2. For Terminal Bench Hard, we directly used Terminus-2, following the same methodology as the Artificial Analysis Intelligence Index to compare North-Mini-Code-1.0 with the other models. Additionally, we used SciCode and LiveCodeBench v6 as complex code-generation benchmarks outside of tool use.</span>
+- <span style="font-size: 80%;">We run each benchmark with 3 different seeds and report the average benchmark performance, using temperature=1.0 and top\_p=0.95. We used publicly reported scores for competitor models, either from original reports or the Artificial Analysis Intelligence Index, where available. Additionally, Gemma4’s scores for agentic coding tasks were reported by [Qwen team](https://qwen.ai/blog?id=qwen3.6-35b-a3b). For benchmark results that any public report is missing, denoted by (\*) in the figure, we run them internally using the recommended model configuration.</span>
+</details>
 **Usage**