Instructions to use catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-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 catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-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 catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF
# Run inference directly in the terminal:
llama cli -hf catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF
# Run inference directly in the terminal:
llama cli -hf catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF

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 catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF
# Run inference directly in the terminal:
./llama-cli -hf catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF

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 catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF

Use Docker

docker model run hf.co/catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF

LM Studio
Jan

vLLM

How to use catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-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": "catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF

Ollama
How to use catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF with Ollama:
```
ollama run hf.co/catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF
```

Unsloth Studio

How to use catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-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 catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-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 catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF to start chatting

Docker Model Runner
How to use catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF with Docker Model Runner:
```
docker model run hf.co/catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF
```

Lemonade

How to use catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF

Run and chat with the model

lemonade run user.CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF

CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF is a quantisation of catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged

This is an instruction fine-tuned model, optimised with DPO, proficient on the following tasks in Catalan

Information extraction (suitable for RAG)
Named Entity Recognition (NER)
Translation from English to Catalan and Catalan to English
Summarization - both short form and long form
Sentiment analysis
Chat

Model developers Laurentiu Petrea based on Llama-3 from Meta.

Model Architecture CataLlama is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and direct preference optimisation (DPO) to align with human preferences for helpfulness and safety.

License The model uses the llama-3 license available at: https://llama.meta.com/llama3/license

Benchmarks (for the bf16 model)

Model	CataLlama-v0.2-Instruct-DPO	CataLlama-v0.2-Instruct-SFT	CataLlama-v0.2-Instruct-SFT-DPO-Merged
MMLU 5 shot	58.89	59.35	60.53
GSM8K CoT 8 shot	60.05	76.04	77.26

Please see the original model card for more details

Intended Use

Note: This model is not intended to beat benchmarks, but to demonstrate techniques for augmenting LLMs on new languages and preserve rare languages as part of our world heritage.

Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.

Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.

**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.