Instructions to use AI4free/jarvis-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AI4free/jarvis-3B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AI4free/jarvis-3B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AI4free/jarvis-3B-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 AI4free/jarvis-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AI4free/jarvis-3B-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 AI4free/jarvis-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AI4free/jarvis-3B-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 AI4free/jarvis-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AI4free/jarvis-3B-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 AI4free/jarvis-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AI4free/jarvis-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AI4free/jarvis-3B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use AI4free/jarvis-3B-GGUF with Ollama:
```
ollama run hf.co/AI4free/jarvis-3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use AI4free/jarvis-3B-GGUF 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 AI4free/jarvis-3B-GGUF 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 AI4free/jarvis-3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AI4free/jarvis-3B-GGUF to start chatting

Docker Model Runner
How to use AI4free/jarvis-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/AI4free/jarvis-3B-GGUF:Q4_K_M
```

Lemonade

How to use AI4free/jarvis-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AI4free/jarvis-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.jarvis-3B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

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Overview

Jarvis-3B is a text generation model developed by Sree and OEvortex. Inspired by the fictional AI assistant Jarvis from the Iron Man series, this model aims to emulate Jarvis's conversational abilities. With a total of 3 billion parameters, Jarvis-3B is designed to handle various natural language understanding and generation tasks.

Model Details

Model Name: Jarvis-3B
Authors: Sree, OEvortex
Parameters: 3 billion
Architecture: Transformers
Training Data: Not specified

Intended Use

Jarvis-3B is intended for tasks requiring text generation, conversational interfaces, and natural language understanding. It can be used in various applications such as chatbots, virtual assistants, and dialogue systems.

Evaluation Results

Evaluation results for Jarvis-3B are pending. Stay tuned for updates.

Downloads last month: -

GGUF

Model size

3B params

Architecture

stablelm

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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AI4free
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jarvis-3B-GGUF

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Overview

Model Details

Intended Use

Evaluation Results

Dataset used to train AI4free/jarvis-3B-GGUF