--- language: - en - ar library_name: transformers license: apache-2.0 pipeline_tag: text-generation base_model: - Qwen/Qwen3-235B-A22B-Instruct-2507 tags: - arabic - saudi-dialect - conversational - qwen3 - mixture-of-experts - long-context - text-generation - ai-agents - enterprise-ai - cloud-ai ---

vivo-c-v1

# vivo-c-v1 ### Balanced Arabic Intelligence for Cloud and Enterprise Applications [🌐 VIVO AI](https://vivo.lahjai.net) • [🤗 Hugging Face](https://huggingface.co/wasmdashai/vivo-c-v1) • [🏢 Lahja AI](https://lahjai.net)
--- ## 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 | Property | Value | |---|---| | Model type | Causal Language Model | | Architecture | Qwen3 Mixture of Experts | | Total parameters | Approximately 235B | | Activated parameters | Approximately 22B per token | | Non-embedding parameters | Approximately 234B | | Number of layers | 94 | | Attention heads | 64 query heads and 4 key-value heads | | Number of experts | 128 | | Activated experts | 8 | | Native context length | 262,144 tokens | | Extended context | Up to approximately 1,010,000 tokens | | Tensor type | BF16 | | License | Apache 2.0 | > 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 | Capability | vivo-c-v1 | General-purpose LLMs | |---|---|---| | Arabic-first deployment focus | High priority | General multilingual coverage | | Saudi and Gulf dialect scenarios | Core target | Varies by model | | Conversational applications | Primary use case | General text generation | | Long-context workflows | Supported | Depends on model | | Enterprise integration | APIs, RAG, tools, knowledge bases | Requires customization | | AI-agent workflows | Designed for tool integration | Varies by implementation | | Cloud deployment | Production-oriented | Depends on infrastructure | | Resource balance | MoE with 22B active parameters | Architecture dependent | --- ## Quick Start Install the latest compatible libraries: ```bash pip install --upgrade "transformers>=4.51.0" accelerate torch ``` Load the model with Hugging Face Transformers: ```python 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 ```bash vllm serve wasmdashai/vivo-c-v1 \ --tensor-parallel-size 8 \ --max-model-len 262144 ``` ### SGLang ```bash 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: ```bash --max-model-len 32768 ``` or: ```bash --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: ```text 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: | Workload | Suggested context | |---|---:| | Standard assistant | 16K–32K | | Enterprise RAG | 32K–128K | | Long-document analysis | 128K–256K | | Specialized ultra-long context | Above 256K with dedicated infrastructure | --- ## Recommended Generation Settings A practical starting configuration is: ```python 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 ```bibtex @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.