Instructions to use mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF",
	filename="Llama-3.1-Tango-8b-Instruct-f16.IQ4_XS.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 mradermacher/Llama-3.1-Tango-8b-Instruct-f16-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 mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Llama-3.1-Tango-8b-Instruct-f16-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 mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Llama-3.1-Tango-8b-Instruct-f16-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 mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Llama-3.1-Tango-8b-Instruct-f16-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 mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/Llama-3.1-Tango-8b-Instruct-f16-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 mradermacher/Llama-3.1-Tango-8b-Instruct-f16-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 mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF to start chatting

How to use mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mradermacher/Llama-3.1-Tango-8b-Instruct-f16-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 mradermacher/Llama-3.1-Tango-8b-Instruct-f16-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 mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Llama-3.1-Tango-8b-Instruct-f16-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.1-Tango-8b-Instruct-f16-GGUF-Q4_K_M

List all available models

lemonade list

Llama-3.1-Tango-8b-Instruct-f16-GGUF

Commit History

uploaded from backup1

f47a744
verified

mradermacher commited on Dec 9, 2024

initial commit

6dd266a
verified

mradermacher commited on Dec 9, 2024

Commit History

uploaded from backup1 f47a744 verified

initial commit 6dd266a verified

uploaded from backup1

f47a744
verified

initial commit

6dd266a
verified