AiCIPPY-Coder

The Agentic Coding Intelligence behind AiCIPPY
by AiVedha · AiVibe Software Services Private Limited

aicippy.com · aivedha.ai · aivibe.cloud · PyPI

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Highlights

We are releasing AiCIPPY-Coder — the open-weight coding intelligence model powering the AiCIPPY agent platform. Built for real-world agentic software development, this model is the foundation of AiCIPPY's CLI and IDE-integrated coding workflows.

• Efficient Yet Powerful: With only 3B activated parameters (80B total), AiCIPPY-Coder delivers performance comparable to models with 10–20x more active parameters — making it highly cost-effective for production agent deployment at scale.
• Advanced Agentic Capabilities: Trained with an elaborate agentic recipe, the model excels at long-horizon reasoning, complex multi-step tool usage, and graceful recovery from execution failures — essential for robust real-world coding tasks.
• Seamless IDE and CLI Integration: A native 256K context window, combined with full adaptability to diverse scaffold templates, enables plug-and-play integration with CLI agents (including AiCIPPY CLI), VS Code extensions, and platforms such as Cline, Kilo, Trae, and others.

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Model Overview

AiCIPPY-Coder carries the following architecture:

Property	Value
Model Type	Causal Language Model
Training Stage	Pretraining & Post-training
Total Parameters	80B
Activated Parameters	3B
Non-Embedding Parameters	79B
Hidden Dimension	2048
Number of Layers	48
Context Length	262,144 tokens (native)
Thinking Mode	Non-thinking (no <think> blocks)

Architecture Details:

• Hybrid Layout: 12 × (3 × Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE)
• Gated Attention: 16 heads for Q, 2 for KV, Head Dim 256, RoPE Dim 64
• Gated DeltaNet: 32 heads for V, 16 for QK, Head Dim 128
• Mixture of Experts: 512 total experts, 10 activated, 1 shared, Expert Intermediate Dim 512

Note: This model operates in non-thinking mode only. The <think></think> output blocks are not generated. Setting enable_thinking=False is not required.

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Quickstart

Ensure you are using the latest version of transformers before proceeding.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "aivedha/aicippy-Coder"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Prepare input
prompt = "Write a quick sort algorithm."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Generate
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=65536
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("AiCIPPY-Coder:", content)

Note: If you encounter out-of-memory (OOM) issues, reduce the context length — for example, to 32,768 tokens.

For local use, AiCIPPY-Coder is compatible with Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers.

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Deployment

AiCIPPY-Coder can be served via sglang or vllm as an OpenAI-compatible API endpoint — the same interface used by the AiCIPPY production platform.

SGLang

SGLang is a fast serving framework for large language and vision language models.

pip install 'sglang[all]>=v0.5.8'

Launch the server with 256K context using tensor parallelism:

python -m sglang.launch_server \
  --model aivedha/aicippy-Coder \
  --port 30000 \
  --tp-size 2 \
  --tool-call-parser aicippy-coder

Note: If the server fails to start, reduce context length with --context-length 32768.

API endpoint available at: http://localhost:30000/v1

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vLLM

vLLM is a high-throughput, memory-efficient inference and serving engine for LLMs.

pip install 'vllm>=0.15.0'

Launch with 256K context:

vllm serve aivedha/aicippy-Coder \
  --port 8000 \
  --tensor-parallel-size 2 \
  --enable-auto-tool-choice \
  --tool-call-parser aicippy-coder

Note: Reduce context length to 32768 if startup fails.

API endpoint available at: http://localhost:8000/v1

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Agentic Coding with AiCIPPY-Coder

AiCIPPY-Coder is purpose-built for tool-calling agentic workflows. Define tools and invoke them directly:

# Tool implementation
def square_the_number(num: float) -> float:
    return num ** 2

# Tool definition
tools = [
    {
        "type": "function",
        "function": {
            "name": "square_the_number",
            "description": "Returns the square of the given number.",
            "parameters": {
                "type": "object",
                "required": ["input_num"],
                "properties": {
                    "input_num": {
                        "type": "number",
                        "description": "The number to be squared."
                    }
                }
            }
        }
    }
]

from openai import OpenAI

# Point to your AiCIPPY-Coder local endpoint
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY"
)

messages = [{"role": "user", "content": "Square the number 1024"}]

completion = client.chat.completions.create(
    messages=messages,
    model="aivedha/aicippy-Coder",
    max_tokens=65536,
    tools=tools,
)

print(completion.choices[0])

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Best Practices

For optimal generation quality, use the following sampling parameters:

Parameter	Recommended Value
temperature	1.0
top_p	0.95
top_k	40

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About AiCIPPY

AiCIPPY is AiVibe's production-grade agentic coding platform — available as a CLI tool on PyPI and deployable on AWS Bedrock. It combines multi-LLM orchestration, persistent memory via DynamoDB, WebSocket streaming, and enterprise SSO via AWS Cognito.

• Platform: aicippy.com
• CLI: pip install aicippy
• Organisation: AiVibe Software Services Private Limited, Chennai, India

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About AiVedha

AiVedha (aivedha.ai) is AiVibe's AI-powered cybersecurity audit and compliance platform — available on AWS Marketplace (prod-kulys2bmix2nm). AiVedha and AiCIPPY together form the core of AiVibe's enterprise AI product portfolio.

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License

This model is released under the Apache 2.0 License. See LICENSE for full terms.

The underlying architecture is derived from Qwen3-Coder-Next (Qwen Team, Alibaba Cloud), used in accordance with its Apache 2.0 license terms.

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Citation

If you use AiCIPPY-Coder in your research or products, please cite:

@misc{aivibe_aicippy_coder_2026,
  title        = {AiCIPPY-Coder: Agentic Coding Intelligence by AiVedha},
  author       = {{AiVibe Software Services Private Limited}},
  year         = {2026},
  url          = {https://huggingface.co/aivedha/aicippy-Coder}}