Instructions to use featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF",
	filename="abacusai-Smaug-Llama-3-70B-Instruct-IQ4_XS/abacusai-Smaug-Llama-3-70B-Instruct-IQ4_XS-00001-of-00008.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use featherless-ai-quants/abacusai-Smaug-Llama-3-70B-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 featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf featherless-ai-quants/abacusai-Smaug-Llama-3-70B-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 featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf featherless-ai-quants/abacusai-Smaug-Llama-3-70B-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 featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf featherless-ai-quants/abacusai-Smaug-Llama-3-70B-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 featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "featherless-ai-quants/abacusai-Smaug-Llama-3-70B-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": "featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF:Q4_K_M

Ollama
How to use featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF with Ollama:
```
ollama run hf.co/featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-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 featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-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 featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull featherless-ai-quants/abacusai-Smaug-Llama-3-70B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.abacusai-Smaug-Llama-3-70B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

abacusai-Smaug-Llama-3-70B-Instruct-GGUF / abacusai-Smaug-Llama-3-70B-Instruct-Q5_K_S /abacusai-Smaug-Llama-3-70B-Instruct-Q5_K_S-00001-of-00010.gguf

m8than

Upload folder using huggingface_hub

7c3fc16 verified over 1 year ago

Download

History Blame GGUF Editor new Delete

4.91 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 481856bd41f1a2f648a02cef8348745b71bd515a0a8a81110df64bb48b471c92
Size of remote file:: 4.91 GB
SHA256:: 0921526608236bb685bd1f5fdbfe44cb9a46907d2996c3cd8e096ddd2c8805b1

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.