Instructions to use KikoCis/AMALIA-9B-0626-SFT-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 KikoCis/AMALIA-9B-0626-SFT-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 KikoCis/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KikoCis/AMALIA-9B-0626-SFT-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 KikoCis/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KikoCis/AMALIA-9B-0626-SFT-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 KikoCis/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KikoCis/AMALIA-9B-0626-SFT-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 KikoCis/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KikoCis/AMALIA-9B-0626-SFT-GGUF:Q4_K_M

Use Docker

docker model run hf.co/KikoCis/AMALIA-9B-0626-SFT-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use KikoCis/AMALIA-9B-0626-SFT-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KikoCis/AMALIA-9B-0626-SFT-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": "KikoCis/AMALIA-9B-0626-SFT-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KikoCis/AMALIA-9B-0626-SFT-GGUF:Q4_K_M

Ollama
How to use KikoCis/AMALIA-9B-0626-SFT-GGUF with Ollama:
```
ollama run hf.co/KikoCis/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
```

Unsloth Studio

How to use KikoCis/AMALIA-9B-0626-SFT-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 KikoCis/AMALIA-9B-0626-SFT-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 KikoCis/AMALIA-9B-0626-SFT-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KikoCis/AMALIA-9B-0626-SFT-GGUF to start chatting

Docker Model Runner
How to use KikoCis/AMALIA-9B-0626-SFT-GGUF with Docker Model Runner:
```
docker model run hf.co/KikoCis/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
```

Lemonade

How to use KikoCis/AMALIA-9B-0626-SFT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KikoCis/AMALIA-9B-0626-SFT-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AMALIA-9B-0626-SFT-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

AMALIA-9B-0626-SFT-GGUF

File size: 504 Bytes

85b16f1

name,size_gb,ppl,ppl_delta_vs_f16,kld_mean,kld_p95,kld_max,top1_match_pct
F16,17.05,4.44,0.0,0.0,0.0,0.0,100.0
Q8_0,9.06,32.115409,0.091330,0.009722,0.031179,3.845082,95.497
Q6_K,7.00,31.491478,-0.532602,0.088915,0.260961,16.998377,86.758
Q5_K_M,6.07,31.717572,-0.306507,0.080225,0.280754,6.408488,86.025
Q4_K_M,5.20,32.896111,0.872032,0.206477,0.734504,13.929957,78.730
IQ4_XS,4.70,30.450651,-1.573428,0.384149,1.645543,30.692392,76.901
Q3_K_M,4.24,35.781539,3.757460,0.420631,1.423969,32.735237,70.642