Instructions to use vikasit-ai/Vikasit-AI-Vikasit-3.5-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vikasit-ai/Vikasit-AI-Vikasit-3.5-4B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vikasit-ai/Vikasit-AI-Vikasit-3.5-4B",
	filename="vikasit-3.5-4b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use vikasit-ai/Vikasit-AI-Vikasit-3.5-4B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M

Use Docker

docker model run hf.co/vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M

LM Studio
Jan
Ollama
How to use vikasit-ai/Vikasit-AI-Vikasit-3.5-4B with Ollama:
```
ollama run hf.co/vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M
```

Unsloth Studio

How to use vikasit-ai/Vikasit-AI-Vikasit-3.5-4B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vikasit-ai/Vikasit-AI-Vikasit-3.5-4B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vikasit-ai/Vikasit-AI-Vikasit-3.5-4B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vikasit-ai/Vikasit-AI-Vikasit-3.5-4B to start chatting

Docker Model Runner
How to use vikasit-ai/Vikasit-AI-Vikasit-3.5-4B with Docker Model Runner:
```
docker model run hf.co/vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M
```

Lemonade

How to use vikasit-ai/Vikasit-AI-Vikasit-3.5-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vikasit-ai/Vikasit-AI-Vikasit-3.5-4B:Q4_K_M

Run and chat with the model

lemonade run user.Vikasit-AI-Vikasit-3.5-4B-Q4_K_M

List all available models

lemonade list

Vikasit 3.5 4B

By Chandorkar Technologies

High-performance AI model optimized for the Indian ecosystem.

Base: Qwen/Qwen3.5-4B
Quantization: Q4_K_M
Format: GGUF

Usage

ollama run vikasit-ai/3.5-4b

Built by Chandorkar Technologies.

Downloads last month: 2

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for vikasit-ai/Vikasit-AI-Vikasit-3.5-4B

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Quantized

(217)

this model