Instructions to use prawinn04/vensa-1.1b-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 prawinn04/vensa-1.1b-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 prawinn04/vensa-1.1b-gguf
# Run inference directly in the terminal:
llama cli -hf prawinn04/vensa-1.1b-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prawinn04/vensa-1.1b-gguf
# Run inference directly in the terminal:
llama cli -hf prawinn04/vensa-1.1b-gguf

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 prawinn04/vensa-1.1b-gguf
# Run inference directly in the terminal:
./llama-cli -hf prawinn04/vensa-1.1b-gguf

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 prawinn04/vensa-1.1b-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prawinn04/vensa-1.1b-gguf

Use Docker

docker model run hf.co/prawinn04/vensa-1.1b-gguf

LM Studio
Jan

vLLM

How to use prawinn04/vensa-1.1b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prawinn04/vensa-1.1b-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": "prawinn04/vensa-1.1b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prawinn04/vensa-1.1b-gguf

Ollama
How to use prawinn04/vensa-1.1b-gguf with Ollama:
```
ollama run hf.co/prawinn04/vensa-1.1b-gguf
```
Unsloth Desktop

How to use prawinn04/vensa-1.1b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prawinn04/vensa-1.1b-gguf

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": "prawinn04/vensa-1.1b-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prawinn04/vensa-1.1b-gguf with Docker Model Runner:
```
docker model run hf.co/prawinn04/vensa-1.1b-gguf
```

Lemonade

How to use prawinn04/vensa-1.1b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prawinn04/vensa-1.1b-gguf

Run and chat with the model

lemonade run user.vensa-1.1b-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use prawinn04/vensa-1.1b-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 prawinn04/vensa-1.1b-gguf

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 prawinn04/vensa-1.1b-gguf

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prawinn04/vensa-1.1b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prawinn04/vensa-1.1b-gguf

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 "prawinn04/vensa-1.1b-gguf" \
  --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"

🛸 Vensa 1.1B (GGUF)

Vensa 1.1B is a lightweight, specialized AI model fine-tuned for Flutter Development and Dart Programming.

This GGUF version is optimized for high-performance inference on local machines and edge devices using llama.cpp. It balances a small footprint with deep knowledge of mobile app architecture and widget implementation.

👤 Developer Information

Developer: Praveen Kumar
Portfolio: praveen-dev.space
Contact: praveenvenkat042k@gmail.com
Expertise: Flutter Development, AI Engineering, and Server Configurations

🛠 Model Details

Architecture: Llama-based (1.1B Parameters)
Format: GGUF (Optimized for CPU & Mobile)
Quantization: Q4_K_M (High efficiency with minimal quality loss)
Primary Focus: Dart syntax, Flutter widget trees, state management (Provider, Riverpod, Bloc), and mobile UI/UX patterns

🔒 API Implementation

While the model weights are open-source (GGUF), the Vensa API is currently a private service used for specialized integrations.

If you are interested in collaboration or custom API access, please contact the developer directly.

💻 How to Use (Python / llama-cpp-python)

To run this model locally, first install the dependencies:

pip install llama-cpp-python huggingface-hub
Then run the following Python script:

Python

from llama_cpp import Llama
from huggingface_hub import hf_hub_download

# 1. Download the GGUF model
model_path = hf_hub_download(
    repo_id="prawinn04/vensa-1.1b-gguf",
    filename="vensa-1.1b.gguf"
)

# 2. Load the model
llm = Llama(
    model_path=model_path,
    n_ctx=2048,
    n_threads=4  # Adjust based on your CPU cores
)

# 3. Run a Flutter-related prompt
output = llm(
    "### Instruction:\nExplain how to use a ListView.builder in Flutter.\n\n### Response:",
    max_tokens=512,
    stop=["### Instruction:"]
)

print(output['choices'][0]['text'])
⚖️ License
This model is released under the MIT License.

Downloads last month: 20

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for prawinn04/vensa-1.1b-gguf

Base model

meta-llama/Llama-3.2-1B

Finetuned

unsloth/Llama-3.2-1B

Quantized

(21)

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