Instructions to use Anoopsingh53/NexAI-v2-3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Anoopsingh53/NexAI-v2-3B-Instruct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Anoopsingh53/NexAI-v2-3B-Instruct-GGUF: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 Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Anoopsingh53/NexAI-v2-3B-Instruct-GGUF: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 Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Anoopsingh53/NexAI-v2-3B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anoopsingh53/NexAI-v2-3B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anoopsingh53/NexAI-v2-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M

Ollama
How to use Anoopsingh53/NexAI-v2-3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Anoopsingh53/NexAI-v2-3B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Anoopsingh53/NexAI-v2-3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use Anoopsingh53/NexAI-v2-3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NexAI-v2-3B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Anoopsingh53/NexAI-v2-3B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Anoopsingh53/NexAI-v2-3B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

🚀 NexAI-v2 (3B Instruct) — GGUF & LoRA

NexAI-v2-3B-Instruct-GGUF is a fine-tuned, instruction-aligned language model based on Qwen/Qwen2.5-3B-Instruct. It is optimized for high-speed reasoning, coding assistance, and ultra-low latency inference on edge devices, browsers, and mobile hardware.

📊 Model Specifications

Base Model: Qwen/Qwen2.5-3B-Instruct
Quantization: Q4_K_M (GGUF via llama.cpp)
File Size: ~1.93 GB (Exact Mobile/Edge sweet spot)
Context Length: 32,768 tokens
License: Apache 2.0

📦 Files in this Repository

nexai-v2-3B-Q4_K_M.gguf: Quantized standalone weights for Ollama, LM Studio, Nirvana Browser, and llama.cpp.
/adapter: LoRA fine-tuned weights and tokenizer config.

💻 How to Use with Ollama

Run directly using Ollama CLI:

ollama run hf.co/Anoopsingh53/NexAI-v2-3B-Instruct-GGUF:nexai-v2-3B-Q4_K_M.gguf

🐍 How to Use with Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="Anoopsingh53/NexAI-v2-3B-Instruct-GGUF",
    filename="nexai-v2-3B-Q4_K_M.gguf",
    n_ctx=4096
)

output = llm("Q: Write a Python quicksort algorithm.\nA:", max_tokens=256)
print(output["choices"][0]["text"])

Downloads last month: 10

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Anoopsingh53/NexAI-v2-3B-Instruct-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(305)

this model