Instructions to use nguyenviet/PhoGPT-7B5-Instruct-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 nguyenviet/PhoGPT-7B5-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 nguyenviet/PhoGPT-7B5-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nguyenviet/PhoGPT-7B5-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 nguyenviet/PhoGPT-7B5-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nguyenviet/PhoGPT-7B5-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 nguyenviet/PhoGPT-7B5-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nguyenviet/PhoGPT-7B5-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 nguyenviet/PhoGPT-7B5-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nguyenviet/PhoGPT-7B5-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use nguyenviet/PhoGPT-7B5-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nguyenviet/PhoGPT-7B5-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nguyenviet/PhoGPT-7B5-Instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF:Q4_K_M

Ollama
How to use nguyenviet/PhoGPT-7B5-Instruct-GGUF with Ollama:
```
ollama run hf.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use nguyenviet/PhoGPT-7B5-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 nguyenviet/PhoGPT-7B5-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 nguyenviet/PhoGPT-7B5-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 nguyenviet/PhoGPT-7B5-Instruct-GGUF to start chatting

Docker Model Runner
How to use nguyenviet/PhoGPT-7B5-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use nguyenviet/PhoGPT-7B5-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nguyenviet/PhoGPT-7B5-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.PhoGPT-7B5-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

PhoGPT-7B5-Instruct.GGUF

GGUF format files of the model vinai/PhoGPT-7B5-Instruct.

Model Details

For detailed information about the original model, please refer to phoGPT's repository.

Uses

Select and download the quantization version that fits the needs.

License

Provided files

Name	Quant method	Size	Use case
PhoGPT-7B5-Instruct-q2_k.gguf	Q2_K	3.8 GB	smallest, significant quality loss - not recommended for most purposes
PhoGPT-7B5-Instruct-q3_k_s.gguf	Q3_K_S	4.07 GB	very small, high quality loss
PhoGPT-7B5-Instruct-q3_k_m.gguf	Q3_K_M	4.66 GB	very small, high quality loss
PhoGPT-7B5-Instruct-q3_k_l.gguf	Q3_K_L	4.98 GB	small, substantial quality loss
PhoGPT-7B5-Instruct-q4_0.gguf	Q4_0	5.06 GB	legacy; small, very high quality loss - prefer using Q3_K_M
PhoGPT-7B5-Instruct-q4_k_s.gguf	Q4_K_S	5.1 GB	small, greater quality loss
PhoGPT-7B5-Instruct-q4_k_m.gguf	Q4_K_M	5.54 GB	medium, balanced quality - recommended
PhoGPT-7B5-Instruct-q4_1.gguf	Q4_1	5.53 GB	legacy; higher accuracy than Q4_0 but not as high as Q5_0, however has quicker inference than Q5 models.
PhoGPT-7B5-Instruct-q5_0.gguf	Q5_0	6 GB	legacy; medium, balanced quality - prefer using Q4_K_M
PhoGPT-7B5-Instruct-q5_k_s.gguf	Q5_K_S	6 GB	large, low quality loss - recommended
PhoGPT-7B5-Instruct-q5_k_m.gguf	Q5_K_M	6.35 GB	large, very low quality loss - recommended
PhoGPT-7B5-Instruct-q5_1.gguf	Q5_1	6.46 GB	legacy; even higher accuracy, resource usage and slower inference.
PhoGPT-7B5-Instruct-q6_k.gguf	Q6_K	6.99 GB	very large, extremely low quality loss
PhoGPT-7B5-Instruct-q8_0.gguf	Q8_0	9.05 GB	almost indistinguishable from float16. High resource use and slow, not recommended for most users
PhoGPT-7B5-Instruct-f16.gguf	float16	17 GB	very large, extremely low quality loss - not recommended

Downloads last month: 286

GGUF

Model size

8B params

Architecture

mpt

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for nguyenviet/PhoGPT-7B5-Instruct-GGUF

Base model

vinai/PhoGPT-7B5-Instruct

Quantized

(1)

this model