Instructions to use deca-ai/2-pro-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deca-ai/2-pro-coder with Transformers:

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

pipe = pipeline("text-generation", model="deca-ai/2-pro-coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deca-ai/2-pro-coder")
model = AutoModelForCausalLM.from_pretrained("deca-ai/2-pro-coder")
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

vLLM

How to use deca-ai/2-pro-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deca-ai/2-pro-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deca-ai/2-pro-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deca-ai/2-pro-coder

SGLang

How to use deca-ai/2-pro-coder 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 "deca-ai/2-pro-coder" \
    --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": "deca-ai/2-pro-coder",
		"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 "deca-ai/2-pro-coder" \
        --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": "deca-ai/2-pro-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deca-ai/2-pro-coder with Docker Model Runner:
```
docker model run hf.co/deca-ai/2-pro-coder
```

The Deca 2 PRO model, currently in BETA, is built on cutting-edge architectures like Perplexity's R1 1776, LLaMA 3, and Qwen 2, delivering extraordinary performance. With a focus on insane speed and high efficiency, Deca 2 PRO is revolutionizing text generation and setting new standards in the industry.

As more capabilities are added, Deca 2 PRO will evolve into a more powerful, any-to-any model in the future. While it’s focused on text generation for now, its foundation is designed to scale, bringing even more advanced functionalities to come.

This model is trained on code-related tasks.

Downloads last month: 8

Safetensors

Model size

69B params

Tensor type

BF16

Model tree for deca-ai/2-pro-coder

Base model

deca-ai/2-pro-base

Finetuned

(1)

this model