Instructions to use QuantFactory/LLaMA-Pro-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/LLaMA-Pro-8B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/LLaMA-Pro-8B-Instruct-GGUF",
	filename="LLaMA-Pro-8B-Instruct.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-Pro-8B-Instruct-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-Pro-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/LLaMA-Pro-8B-Instruct-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-Pro-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/LLaMA-Pro-8B-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 QuantFactory/LLaMA-Pro-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/LLaMA-Pro-8B-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 QuantFactory/LLaMA-Pro-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/LLaMA-Pro-8B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/LLaMA-Pro-8B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/LLaMA-Pro-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/LLaMA-Pro-8B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/LLaMA-Pro-8B-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 QuantFactory/LLaMA-Pro-8B-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 QuantFactory/LLaMA-Pro-8B-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 QuantFactory/LLaMA-Pro-8B-Instruct-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/LLaMA-Pro-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/LLaMA-Pro-8B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/LLaMA-Pro-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/LLaMA-Pro-8B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LLaMA-Pro-8B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/LLaMA-Pro-8B-Instruct-GGUF

This is quantized version of TencentARC/LLaMA-Pro-8B-Instruct created using llama.cpp

Original Model Card

LLaMA-PRO-Instruct Model Card

Model Description

LLaMA-PRO-Instruct is a transformative expansion of the LLaMA2-7B model, now boasting 8.3 billion parameters. It uniquely specializes in programming, coding, and mathematical reasoning, maintaining versatility in general language tasks.

Development and Training

This model, developed by Tencent ARC team, extends LLaMA2-7B using innovative block expansion techniques. It's meticulously trained on a diverse blend of coding and mathematical data, encompassing over 80 billion tokens.

Intended Use

LLaMA-PRO-Instruct is ideal for complex NLP challenges, excelling in programming, mathematical reasoning, and general language processing, suitable for both specialized and broad applications.

Performance

It surpasses its predecessors in the LLaMA series, especially in code domains, demonstrating exceptional competence as a comprehensive language model.

Limitations

Despite advancements, it may encounter difficulties in highly niche or nuanced tasks.

Ethical Considerations

Users are advised to consider inherent biases and responsibly manage its application across various fields.

Downloads last month: 48

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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