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
```

system commited on Mar 13, 2025

Commit

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1 Parent(s): f102123

Update README.md

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@@ -1,40 +1,19 @@
 ---
 base_model:
-- Blazgo/2-pro-base
-- NobodyExistsOnTheInternet/code-llama-70b-python-instruct
 library_name: transformers
 tags:
-- mergekit
 - merge
 ---
-# merge
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
-### Models Merged
-The following models were included in the merge:
-* [Blazgo/2-pro-base](https://huggingface.co/Blazgo/2-pro-base)
-* [NobodyExistsOnTheInternet/code-llama-70b-python-instruct](https://huggingface.co/NobodyExistsOnTheInternet/code-llama-70b-python-instruct)
-### Configuration
-The following YAML configuration was used to produce this model:
-```yaml
-models:
-  - model: Blazgo/2-pro-base
-  - model: NobodyExistsOnTheInternet/code-llama-70b-python-instruct
-merge_method: slerp
-base_model: Blazgo/2-pro-base
-dtype: bfloat16
-parameters:
-  t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
-```

 ---
 base_model:
+- deca-ai/2-pro-base
 library_name: transformers
 tags:
 - merge
+- reasoning
+- R1
+- Deca
+- Deca-AI
+- uncensored
 ---
+![Deca 2 Banner](https://huggingface.co/deca-ai/2-mini-beta/resolve/main/banner.jpg)
+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.