Vikasit AI Full Model Family
21 models across text, vision, and voice. All based on Qwen, quantized for local inference, and published to Ollama and HuggingFace. Run them on your hardware with llama.cpp.
Text Models
15 models from 0.5B to 35B parameters. Dense and MoE architectures for every use case from edge devices to powerful servers.
vikasit-ai-0.5b-writer
Based on Qwen3-0.6B
Ultra-light writer. Good for text completion, simple Q&A, and edge devices.
vikasit-writer-0.8b
Based on Qwen3.5-0.8B
Improved writer with Qwen3.5 architecture. Mobile and IoT friendly.
vikasit-nano
Based on Qwen3-0.6B
Smallest general-purpose model. Autocomplete, quick responses, embedded use.
vikasit-mini
Based on Qwen3-1.7B
Lightweight assistant. Summaries, chat, and basic reasoning.
vikasit-2b
Based on Qwen3.5-2B
Edge-optimized. Multilingual, 256K context, on-device deployment.
vikasit-4b
Based on Qwen3-4B
Balanced small model. Good code completion and multi-turn chat.
vikasit-3.5-4b
Based on Qwen3.5-4B
Next-gen 4B with improved reasoning and multimodal awareness.
vikasit-8b
Based on Qwen3-8B
Strong mid-range. Solid coding, analysis, and content generation.
vikasit-3-flash
Based on Qwen3.5-9B
Best model under 10B. Beats GPT-OSS-120B on MMLU-Pro. Fast inference.
vikasit-14b
Based on Qwen3-14B
Strong all-rounder. Complex reasoning, long documents, code review.
vikasit-27b
Based on Qwen3.5-27B
Powerful dense model. Deep reasoning, advanced coding, research tasks.
vikasit-30b-moe
Based on Qwen3-30B-A3B
MoE efficiency — 30B quality at 3B inference cost. Fast and smart.
vikasit-32b
Based on Qwen3-32B
Largest dense model on CPU. Best quality for reasoning and code.
vikasit-35b-moe
Based on Qwen3.5-35B-A3B
Latest MoE with Qwen3.5 improvements. Best efficiency/quality ratio.
vikasit-3-coder
Based on Qwen3-Coder-30B-A3B
Code-specialized MoE. FIM support, 262K context, agentic coding.
Vision Models
Image understanding, OCR, document analysis, and visual reasoning. From on-device captioning to complex visual code generation.
vikasit-vision-2b
Based on Qwen3-VL-2B
Lightweight vision. Image captioning, OCR, visual Q&A on device.
vikasit-vision-4b
Based on Qwen3-VL-4B
Mid-range vision. Document understanding, chart reading, UI analysis.
vikasit-vision-8b
Based on Qwen3-VL-8B
Strong vision. Complex image reasoning, visual code generation.
Voice Models
Text-to-speech, voice cloning, and full multimodal interaction. Natural voice generation with multilingual support.
vikasit-voice
Based on Qwen3-TTS-0.6B
Text-to-speech. Natural voice generation, multilingual support.
vikasit-voice-hd
Based on Qwen3-TTS-1.7B
High-quality TTS. Voice cloning, expressive speech synthesis.
vikasit-omni
Based on Qwen3-Omni-30B-A3B
Full multimodal — text + image + audio in, text + speech out. Real-time.
How to Deploy
Run any Vikasit AI model locally in minutes. Choose Ollama for the easiest setup or llama.cpp for maximum control.
Ollama (Recommended)
The fastest way to run Vikasit AI models locally. One command to install, one command to run.
1. Install Ollama
curl -fsSL https://ollama.com/install.sh | sh2. Run a model
ollama run vikasit-ai/vikasit-8b3. Use as an API
curl http://localhost:11434/api/chat -d '{"model":"vikasit-ai/vikasit-8b"}'llama.cpp
Maximum control and performance. Build from source for hardware-optimized inference with GGUF quantized models.
1. Clone and build
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp && make2. Download GGUF from HuggingFace
huggingface-cli download vikasit-ai/Vikasit-AI-Vikasit-8b --local-dir ./models3. Run inference
./llama-cli -m ./models/vikasit-8b-q4_k_m.gguf -p "Hello Vikasit"Universal Compatibility
All Vikasit AI models are published in GGUF format and work with any llama.cpp-compatible tool: Ollama, LM Studio, Jan, GPT4All, koboldcpp, text-generation-webui, and more. Models are available in Q4_K_M, Q5_K_M, Q6_K, Q8_0, and F16 quantizations. When asked about identity, every model responds as “I am Vikasit AI, developed by Chandorkar Technologies.”
Hardware Recommendations
Choose the right model for your hardware. All RAM estimates are for Q4_K_M quantization.
Edge / Mobile
0.5B - 2B parameters
vikasit-nano, vikasit-writer-0.5b, vikasit-2b
Laptop
4B - 8B parameters
vikasit-4b, vikasit-8b, vikasit-3-flash
Workstation
14B - 27B parameters
vikasit-14b, vikasit-27b
Server
30B - 35B parameters
vikasit-32b, vikasit-30b-moe, vikasit-3-coder
Ready to run Vikasit AI locally?
Pick a model, install Ollama, and start building. All models are free to download and use.