Instructions to use featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-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 featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-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/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-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/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-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/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-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/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF:Q4_K_M

Use Docker

docker model run hf.co/featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-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/gradientai-Llama-3-8B-Instruct-Gradient-1048k-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/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF:Q4_K_M

Ollama
How to use featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF with Ollama:
```
ollama run hf.co/featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF with Docker Model Runner:
```
docker model run hf.co/featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF:Q4_K_M
```

Lemonade

How to use featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull featherless-ai-quants/gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

gradientai-Llama-3-8B-Instruct-Gradient-1048k-GGUF / gradientai-Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M.gguf

m8than

Upload folder using huggingface_hub

d6a5f1d verified almost 2 years ago

Download

History Blame GGUF Editor new Delete

5.73 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:: 101c2bc5c2196dbd8cc16b02c4a4e51ce04dda60213e6b6d5a0a603bf3fce847
Size of remote file:: 5.73 GB
SHA256:: 478cfb32b1a92457d145ddcd76625e644ddd2f029940d5851e7b830eec732b2f

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