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
- llama.cpp
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
- Pi
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
| 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. |