wasmdashai/vivo-c-v1

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

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vivo-c-v1

Balanced Arabic Intelligence for Cloud and Enterprise Applications

🌐 VIVO AI • 🤗 Hugging Face • 🏢 Lahja AI

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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.