vivo-c-v1

<p align="center"> <img src="./vivo.png" alt="vivo-c-v1" width="100%"> </p> <div align="center">

vivo-c-v1

Balanced Arabic Intelligence for Cloud and Enterprise Applications

🌐 VIVO AI • 🤗 Hugging Face • 🏢 Lahja AI

</div>

---

Overview

vivo-c-v1 is a large-scale conversational language model developed as part of the VIVO AI model family.

The model is built on Qwen3-235B-A22B-Instruct-2507, a Mixture-of-Experts causal language model with approximately 235 billion total parameters and 22 billion activated parameters per token.

vivo-c-v1 is being developed to provide a balanced foundation for Arabic conversational intelligence, enterprise assistants, AI agents, knowledge-based systems, cloud applications, and long-context workloads.

The model places particular emphasis on:

• Modern Standard Arabic
• Saudi and Gulf Arabic dialects
• Natural conversational interaction
• Long-context understanding
• Enterprise knowledge integration
• Tool and function calling
• Cloud-native deployment
• AI-agent workflows

---

Why vivo-c-v1?

Many general-purpose language models are optimized primarily for broad multilingual benchmarks. vivo-c-v1 is positioned around practical deployment scenarios where response quality, contextual continuity, scalability, and integration with external systems are essential.

The model is designed for applications that require:

• Natural Arabic conversations
• Regional dialect awareness
• Persistent conversational context
• Retrieval-Augmented Generation (RAG)
• Enterprise knowledge bases
• API and tool integration
• Intelligent workflow automation
• Scalable cloud inference

---

Model Architecture

The parameter and architecture values above describe the underlying base architecture. They should not be interpreted as independently reproduced performance claims for vivo-c-v1.

---

Core Capabilities

Arabic and Regional Dialects

vivo-c-v1 is intended to improve interactions for Arabic-speaking users by focusing on:

• Modern Standard Arabic
• Saudi Arabic dialects
• Gulf dialects
• Context-aware Arabic responses
• Reduced literal translation
• Better regional language adaptation
• Arabic instruction following

Long-Context Understanding

The underlying architecture supports a native context length of 262,144 tokens and can be extended to approximately 1 million tokens using supported long-context configurations.

This makes the model suitable for:

• Long enterprise documents
• Large knowledge bases
• Extended conversations
• Repository-level code analysis
• Research and technical documents
• Multi-step agent workflows

Enterprise and Agentic AI

vivo-c-v1 is designed for integration into:

• AI customer-service platforms
• Smart virtual assistants
• Enterprise search
• Knowledge management
• AI agents
• Tool-calling systems
• RAG pipelines
• Workflow automation
• Government, education, and healthcare applications

---

Comparison with General Language Models

---

Quick Start

Install the latest compatible libraries:

pip install --upgrade "transformers>=4.51.0" accelerate torch

Load the model with Hugging Face Transformers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "wasmdashai/vivo-c-v1"

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True,
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {
        "role": "system",
        "content": (
            "You are an enterprise AI assistant specialized in Arabic "
            "and Saudi conversational applications."
        ),
    },
    {
        "role": "user",
        "content": "صمم معمارية لمنصة مساعد ذكي مؤسسية قابلة للتوسع.",
    },
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer(
    [text],
    return_tensors="pt",
).to(model.device)

with torch.inference_mode():
    generated_ids = model.generate(
        **inputs,
        max_new_tokens=2048,
        temperature=0.7,
        top_p=0.8,
        top_k=20,
        do_sample=True,
    )

output_ids = generated_ids[0][inputs.input_ids.shape[-1]:]

response = tokenizer.decode(
    output_ids,
    skip_special_tokens=True,
)

print(response)

---

Cloud Deployment

vLLM

vllm serve wasmdashai/vivo-c-v1 \
  --tensor-parallel-size 8 \
  --max-model-len 262144

SGLang

python -m sglang.launch_server \
  --model-path wasmdashai/vivo-c-v1 \
  --tp 8 \
  --context-length 262144

For environments with limited GPU memory, reduce the context length:

--max-model-len 32768

or:

--context-length 32768

---

Agentic Use

vivo-c-v1 can be integrated with:

• Function calling
• Model Context Protocol (MCP)
• Code execution tools
• Search and retrieval tools
• RAG systems
• Enterprise APIs
• Workflow orchestration
• Multi-agent applications

A typical production architecture may include:

User Interface
      │
      ▼
API Gateway
      │
      ▼
vivo-c-v1 Inference Service
      │
      ├── RAG and Vector Database
      ├── Enterprise Knowledge Bases
      ├── External APIs and Tools
      ├── Agent Orchestration
      └── Monitoring and Safety Layer

---

Long-Context Deployment

Processing contexts close to one million tokens requires substantial infrastructure for:

• Model weights
• KV cache
• Activation memory
• Tensor parallelism
• High-bandwidth GPU communication

For production deployment, context size should be selected according to the actual workload rather than always enabling the maximum supported length.

Recommended starting values:

---

Recommended Generation Settings

A practical starting configuration is:

generation_config = {
    "temperature": 0.7,
    "top_p": 0.8,
    "top_k": 20,
    "max_new_tokens": 2048,
    "do_sample": True,
}

These values should be adjusted according to the application, latency target, output length, and factuality requirements.

---

Intended Uses

vivo-c-v1 is intended for:

• Arabic conversational assistants
• Saudi and Gulf customer-service applications
• Enterprise copilots
• AI agents and automation
• Knowledge-base assistants
• RAG systems
• Long-document analysis
• Code and technical assistance
• Government digital services
• Education and healthcare platforms
• Cloud-native AI applications

---

Limitations

• The model may generate inaccurate or unsupported information.
• Arabic dialect quality may vary by topic and prompt.
• Long-context support does not guarantee perfect recall of every detail.
• Large-scale inference requires substantial GPU infrastructure.
• Generated code and business recommendations should be reviewed.
• Sensitive or high-stakes outputs require human validation.
• Performance results of the base model should not be presented as independently reproduced vivo-c-v1 results unless separate evaluations are published.

---

Responsible Use

Users are responsible for:

• Reviewing generated content
• Protecting personal and confidential information
• Applying appropriate access controls
• Monitoring production outputs
• Testing integrations before deployment
• Complying with applicable laws and organizational policies

---

Platform Links

vivo-c-v1 on Hugging Face

https://huggingface.co/wasmdashai/vivo-c-v1

VIVO AI Platform

https://vivo.lahjai.net

Lahja AI

https://lahjai.net

---

Base Model and Attribution

vivo-c-v1 is based on:

Qwen3-235B-A22B-Instruct-2507

https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507

Please follow the applicable base-model license and attribution requirements.

---

Citation

@misc{qwen3technicalreport,
    title        = {Qwen3 Technical Report},
    author       = {Qwen Team},
    year         = {2025},
    eprint       = {2505.09388},
    archivePrefix= {arXiv},
    primaryClass = {cs.CL}
}

---

License

This repository uses the Apache License 2.0, subject to the applicable terms of the base model and included dependencies.