Instructions to use zidsi/Zlatorog-12B-Instruct-Beta-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zidsi/Zlatorog-12B-Instruct-Beta-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="zidsi/Zlatorog-12B-Instruct-Beta-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("zidsi/Zlatorog-12B-Instruct-Beta-GGUF", dtype="auto")

llama-cpp-python

How to use zidsi/Zlatorog-12B-Instruct-Beta-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="zidsi/Zlatorog-12B-Instruct-Beta-GGUF",
	filename="zlatorog_12b_v6_Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use zidsi/Zlatorog-12B-Instruct-Beta-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf zidsi/Zlatorog-12B-Instruct-Beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf zidsi/Zlatorog-12B-Instruct-Beta-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 zidsi/Zlatorog-12B-Instruct-Beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf zidsi/Zlatorog-12B-Instruct-Beta-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 zidsi/Zlatorog-12B-Instruct-Beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf zidsi/Zlatorog-12B-Instruct-Beta-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 zidsi/Zlatorog-12B-Instruct-Beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zidsi/Zlatorog-12B-Instruct-Beta-GGUF:Q4_K_M

Use Docker

docker model run hf.co/zidsi/Zlatorog-12B-Instruct-Beta-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use zidsi/Zlatorog-12B-Instruct-Beta-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zidsi/Zlatorog-12B-Instruct-Beta-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": "zidsi/Zlatorog-12B-Instruct-Beta-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zidsi/Zlatorog-12B-Instruct-Beta-GGUF:Q4_K_M

SGLang

How to use zidsi/Zlatorog-12B-Instruct-Beta-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 "zidsi/Zlatorog-12B-Instruct-Beta-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": "zidsi/Zlatorog-12B-Instruct-Beta-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 "zidsi/Zlatorog-12B-Instruct-Beta-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": "zidsi/Zlatorog-12B-Instruct-Beta-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use zidsi/Zlatorog-12B-Instruct-Beta-GGUF with Ollama:
```
ollama run hf.co/zidsi/Zlatorog-12B-Instruct-Beta-GGUF:Q4_K_M
```

Unsloth Studio new

How to use zidsi/Zlatorog-12B-Instruct-Beta-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 zidsi/Zlatorog-12B-Instruct-Beta-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 zidsi/Zlatorog-12B-Instruct-Beta-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zidsi/Zlatorog-12B-Instruct-Beta-GGUF to start chatting

Docker Model Runner
How to use zidsi/Zlatorog-12B-Instruct-Beta-GGUF with Docker Model Runner:
```
docker model run hf.co/zidsi/Zlatorog-12B-Instruct-Beta-GGUF:Q4_K_M
```

Lemonade

How to use zidsi/Zlatorog-12B-Instruct-Beta-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zidsi/Zlatorog-12B-Instruct-Beta-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Zlatorog-12B-Instruct-Beta-GGUF-Q4_K_M

List all available models

lemonade list

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Zlatorog-12B-Instruct-Beta

This model is a fine-tuned version of zidsi/MistralNemoCPT6 on the custom mix of SFT datasets.

Model description

More information needed

Intended uses & limitations

Research explore and have fun with Slovenian LLM :)

Training and evaluation data

Bad standard Slovenian benchmarks results but sometimes impresssive "real world" prompt responses :)

Reduced hallucinations rate on "Who is ...?" prompts.

Tools use to be evaluated

Up to 16k ctx should work OK, for longer contexts training data would be required to improve CPT Long stage

More information needed

GGUF

The HF model was coverted to GGUF using llama.cpp

Downloads last month: 2

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for zidsi/Zlatorog-12B-Instruct-Beta-GGUF

Base model

zidsi/MistralNemoCPT6

Quantized

(1)

this model