Instructions to use Eram83/test_slo_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Eram83/test_slo_1 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 Eram83/test_slo_1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Eram83/test_slo_1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Eram83/test_slo_1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Eram83/test_slo_1: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 Eram83/test_slo_1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Eram83/test_slo_1: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 Eram83/test_slo_1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Eram83/test_slo_1:Q4_K_M

Use Docker

docker model run hf.co/Eram83/test_slo_1:Q4_K_M

LM Studio
Jan
Ollama
How to use Eram83/test_slo_1 with Ollama:
```
ollama run hf.co/Eram83/test_slo_1:Q4_K_M
```

Unsloth Studio

How to use Eram83/test_slo_1 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 Eram83/test_slo_1 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 Eram83/test_slo_1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Eram83/test_slo_1 to start chatting

How to use Eram83/test_slo_1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Eram83/test_slo_1:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Eram83/test_slo_1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Eram83/test_slo_1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Eram83/test_slo_1:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Eram83/test_slo_1:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Eram83/test_slo_1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Eram83/test_slo_1:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Eram83/test_slo_1:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Eram83/test_slo_1 with Docker Model Runner:
```
docker model run hf.co/Eram83/test_slo_1:Q4_K_M
```

Lemonade

How to use Eram83/test_slo_1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Eram83/test_slo_1:Q4_K_M

Run and chat with the model

lemonade run user.test_slo_1-Q4_K_M

List all available models

lemonade list

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Check out the documentation for more information.

Slovenian Fine-Tuning Attempt

This repository contains a fine-tuned version of Llama-3.2-3b that was used as a first attempt to teach the model the Slovenian language.

The goal of this project was to explore how well the base model can adapt to Slovenian through supervised fine-tuning and to evaluate the quality of generated responses in a low-resource language setting.

Motivation

Slovenian is a relatively low-resource language compared to major global languages, so this experiment was designed to test whether a general-purpose language model can be adapted to produce more natural and useful Slovenian text with limited fine-tuning data.

What this model is for

This model was trained as an early experiment for:

Testing Slovenian language generation.
Evaluating instruction-following behavior in Slovenian.
Exploring domain adaptation for local-language use cases.
Building a foundation for future, improved fine-tuning runs.

Important Notes

This is not a production-ready model.
It is an early experimental checkpoint and may still produce:

grammatical errors,
unnatural phrasing,
inconsistent Slovenian vocabulary,
occasional mixed-language output,
hallucinations or incorrect responses.

The results should be treated as a baseline for further development rather than a final language model.

Future Work

Planned improvements include:

expanding the Slovenian dataset,
improving grammar and fluency,
testing different fine-tuning strategies,
comparing outputs against the base model,
creating a more robust instruction-tuned version.

License

This repository follows the license of the base model and the terms of the training data used.
Please review the original model license before using this checkpoint in downstream applications.

Acknowledgements

Thanks to the open-source AI community and the creators of the base model used in this experiment.

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Architecture

llama

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