Instructions to use datatab/YugoGPT-Quantized-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use datatab/YugoGPT-Quantized-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("datatab/YugoGPT-Quantized-GGUF", dtype="auto")

llama-cpp-python

How to use datatab/YugoGPT-Quantized-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="datatab/YugoGPT-Quantized-GGUF",
	filename="YugoGPT-Quantized-GGUF.Q3_K_XS.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use datatab/YugoGPT-Quantized-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf datatab/YugoGPT-Quantized-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf datatab/YugoGPT-Quantized-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 datatab/YugoGPT-Quantized-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf datatab/YugoGPT-Quantized-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 datatab/YugoGPT-Quantized-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf datatab/YugoGPT-Quantized-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 datatab/YugoGPT-Quantized-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf datatab/YugoGPT-Quantized-GGUF:Q4_K_M

Use Docker

docker model run hf.co/datatab/YugoGPT-Quantized-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use datatab/YugoGPT-Quantized-GGUF with Ollama:
```
ollama run hf.co/datatab/YugoGPT-Quantized-GGUF:Q4_K_M
```

Unsloth Studio new

How to use datatab/YugoGPT-Quantized-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 datatab/YugoGPT-Quantized-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 datatab/YugoGPT-Quantized-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for datatab/YugoGPT-Quantized-GGUF to start chatting

Docker Model Runner
How to use datatab/YugoGPT-Quantized-GGUF with Docker Model Runner:
```
docker model run hf.co/datatab/YugoGPT-Quantized-GGUF:Q4_K_M
```

Lemonade

How to use datatab/YugoGPT-Quantized-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull datatab/YugoGPT-Quantized-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.YugoGPT-Quantized-GGUF-Q4_K_M

List all available models

lemonade list

YugoGPT-Quantized-GGUF

Quantized by: datatab
License: apache-2.0
Author of model : gordicaleksa/YugoGPT

Description

This repo contains GGUF format model files for YugoGPT.

Quant. preference

Quant.	Description
not_quantized	Recommended. Fast conversion. Slow inference, big files.
fast_quantized	Recommended. Fast conversion. OK inference, OK file size.
quantized	Recommended. Slow conversion. Fast inference, small files.
f32	Not recommended. Retains 100% accuracy, but super slow and memory hungry.
f16	Fastest conversion + retains 100% accuracy. Slow and memory hungry.
q8_0	Fast conversion. High resource use, but generally acceptable.
q4_k_m	Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
q5_k_m	Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
q2_k	Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
q3_k_l	Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
q3_k_m	Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
q3_k_s	Uses Q3_K for all tensors
q4_0	Original quant method, 4-bit.
q4_1	Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
q4_k_s	Uses Q4_K for all tensors
q4_k	alias for q4_k_m
q5_k	alias for q5_k_m
q5_0	Higher accuracy, higher resource usage and slower inference.
q5_1	Even higher accuracy, resource usage and slower inference.
q5_k_s	Uses Q5_K for all tensors
q6_k	Uses Q8_K for all tensors
iq2_xxs	2.06 bpw quantization
iq2_xs	2.31 bpw quantization
iq3_xxs	3.06 bpw quantization
q3_k_xs	3-bit extra small quantization

Downloads last month: 294

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for datatab/YugoGPT-Quantized-GGUF

Base model

gordicaleksa/YugoGPT

Quantized

(11)

this model

Collection including datatab/YugoGPT-Quantized-GGUF

Yugo-GPT

Collection

Yugo-GPT class of LLM (45, 55, 60) • 12 items • Updated Mar 2