Instructions to use QuantFactory/llama-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/llama-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/llama-7b-GGUF",
	filename="llama-7b.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/llama-7b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/llama-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/llama-7b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/llama-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/llama-7b-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 QuantFactory/llama-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/llama-7b-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 QuantFactory/llama-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/llama-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/llama-7b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/llama-7b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/llama-7b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/llama-7b-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 QuantFactory/llama-7b-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 QuantFactory/llama-7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/llama-7b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/llama-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/llama-7b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/llama-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/llama-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama-7b-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/llama-7b-GGUF

This is quantized version of huggyllama/llama-7b created using llama.cpp

Original Model Card

This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file). You should only use this repository if you have been granted access to the model by filling out this form but either lost your copy of the weights or got some trouble converting them to the Transformers format.

Downloads last month: 45

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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