Instructions to use meissosisai/Arc1-Coder-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meissosisai/Arc1-Coder-14b with Transformers:

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

pipe = pipeline("text-generation", model="meissosisai/Arc1-Coder-14b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meissosisai/Arc1-Coder-14b")
model = AutoModelForCausalLM.from_pretrained("meissosisai/Arc1-Coder-14b", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meissosisai/Arc1-Coder-14b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meissosisai/Arc1-Coder-14b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meissosisai/Arc1-Coder-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meissosisai/Arc1-Coder-14b

SGLang

How to use meissosisai/Arc1-Coder-14b 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 "meissosisai/Arc1-Coder-14b" \
    --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": "meissosisai/Arc1-Coder-14b",
		"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 "meissosisai/Arc1-Coder-14b" \
        --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": "meissosisai/Arc1-Coder-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meissosisai/Arc1-Coder-14b with Docker Model Runner:
```
docker model run hf.co/meissosisai/Arc1-Coder-14b
```

Arc1-Coder-14b

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(Trained with Unsloth) c1e18d6 verified

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initial commit 3ff2ef1 verified

Update README.md

30ac604
verified

Update README.md

c29349e
verified

Update README.md

d475d51
verified

Update README.md

494f610
verified

Update README.md

6e52054
verified

Update README.md

64691b9
verified

Update README.md

dcd054d
verified

Update README.md

63afde2
verified

(Trained with Unsloth)

c1e18d6
verified

Unsloth Model Card

adb4e71
verified

initial commit

3ff2ef1
verified