Instructions to use roleplaiapp/AceInstruct-7B-Q4_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use roleplaiapp/AceInstruct-7B-Q4_0-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="roleplaiapp/AceInstruct-7B-Q4_0-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("roleplaiapp/AceInstruct-7B-Q4_0-GGUF", dtype="auto")

llama-cpp-python

How to use roleplaiapp/AceInstruct-7B-Q4_0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="roleplaiapp/AceInstruct-7B-Q4_0-GGUF",
	filename="aceinstruct-7b-q4_0.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 roleplaiapp/AceInstruct-7B-Q4_0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0

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 roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0

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 roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0

Use Docker

docker model run hf.co/roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use roleplaiapp/AceInstruct-7B-Q4_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "roleplaiapp/AceInstruct-7B-Q4_0-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": "roleplaiapp/AceInstruct-7B-Q4_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0

SGLang

How to use roleplaiapp/AceInstruct-7B-Q4_0-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "roleplaiapp/AceInstruct-7B-Q4_0-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "roleplaiapp/AceInstruct-7B-Q4_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "roleplaiapp/AceInstruct-7B-Q4_0-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "roleplaiapp/AceInstruct-7B-Q4_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use roleplaiapp/AceInstruct-7B-Q4_0-GGUF with Ollama:
```
ollama run hf.co/roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0
```

Unsloth Studio

How to use roleplaiapp/AceInstruct-7B-Q4_0-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 roleplaiapp/AceInstruct-7B-Q4_0-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 roleplaiapp/AceInstruct-7B-Q4_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for roleplaiapp/AceInstruct-7B-Q4_0-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use roleplaiapp/AceInstruct-7B-Q4_0-GGUF with Docker Model Runner:
```
docker model run hf.co/roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0
```

Lemonade

How to use roleplaiapp/AceInstruct-7B-Q4_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull roleplaiapp/AceInstruct-7B-Q4_0-GGUF:Q4_0

Run and chat with the model

lemonade run user.AceInstruct-7B-Q4_0-GGUF-Q4_0

List all available models

lemonade list

roleplaiapp/AceInstruct-7B-Q4_0-GGUF

Repo: roleplaiapp/AceInstruct-7B-Q4_0-GGUF
Original Model: AceInstruct-7B Organization: nvidia Quantized File: aceinstruct-7b-q4_0.gguf Quantization: GGUF Quantization Method: Q4_0
Use Imatrix: False
Split Model: False

Overview

This is an GGUF Q4_0 quantized version of AceInstruct-7B.

Quantization By

I often have idle A100 GPUs while building/testing and training the RP app, so I put them to use quantizing models. I hope the community finds these quantizations useful.

Andrew Webby @ RolePlai

Downloads last month: 1

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for roleplaiapp/AceInstruct-7B-Q4_0-GGUF

Base model

nvidia/AceInstruct-7B

Quantized

(22)

this model