Instructions to use developerJenis/Artha-v1-E2B-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 developerJenis/Artha-v1-E2B-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 developerJenis/Artha-v1-E2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf developerJenis/Artha-v1-E2B-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 developerJenis/Artha-v1-E2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf developerJenis/Artha-v1-E2B-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 developerJenis/Artha-v1-E2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf developerJenis/Artha-v1-E2B-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 developerJenis/Artha-v1-E2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf developerJenis/Artha-v1-E2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/developerJenis/Artha-v1-E2B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use developerJenis/Artha-v1-E2B-GGUF with Ollama:
```
ollama run hf.co/developerJenis/Artha-v1-E2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use developerJenis/Artha-v1-E2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf developerJenis/Artha-v1-E2B-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": "developerJenis/Artha-v1-E2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use developerJenis/Artha-v1-E2B-GGUF with Docker Model Runner:
```
docker model run hf.co/developerJenis/Artha-v1-E2B-GGUF:Q4_K_M
```

Lemonade

How to use developerJenis/Artha-v1-E2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull developerJenis/Artha-v1-E2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Artha-v1-E2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use developerJenis/Artha-v1-E2B-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 developerJenis/Artha-v1-E2B-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 developerJenis/Artha-v1-E2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use developerJenis/Artha-v1-E2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf developerJenis/Artha-v1-E2B-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 "developerJenis/Artha-v1-E2B-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"

Artha v1 E2B (GGUF)

GGUF build of Artha v1 E2B: a compact writing model that turns rough English, Hinglish or Gujlish requests into ready-to-send emails, WhatsApp messages and LinkedIn posts that read like a real person wrote them.

See the main model card for examples, prompting, evaluation and limitations.

Files

File	Description
`Artha-v1-E2B.Q4_K_M.gguf`	4-bit quantised model, recommended for laptops and desktops
`Artha-v1-E2B.mmproj-BF16.gguf`	Vision projector inherited from Gemma 4. Optional; Artha is tuned for text
`Modelfile`	Ollama Modelfile with the chat template and recommended settings

Run it

Ollama

ollama run hf.co/developerJenis/Artha-v1-E2B-GGUF:Q4_K_M

Or with the included Modelfile, after downloading the files:

ollama create artha -f Modelfile
ollama run artha

llama.cpp

llama-cli -hf developerJenis/Artha-v1-E2B-GGUF:Q4_K_M --jinja

LM Studio: search for developerJenis/Artha-v1-E2B-GGUF and load the Q4_K_M file.

Recommended sampling: temperature 0.7, top_p 0.9.

Try this

client ne 45 din se 2.4 lakh ka payment nahi kiya, firm mail likho. client Mehta Traders, contact Rakesh, mera naam Jenis

License

Apache 2.0, the same license as the Gemma 4 base model. Built by Jenis (developerJenis).

Downloads last month: 131

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

Inference Providers NEW

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

Model tree for developerJenis/Artha-v1-E2B-GGUF

Base model

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Finetuned

google/gemma-4-E2B-it

Finetuned

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Quantized

(3)

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