Instructions to use swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_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 swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_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 swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_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 swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_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 swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF:Q4_K_M
Use Docker
docker model run hf.co/swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_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": "swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF:Q4_K_M
- SGLang
How to use swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_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 "swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_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": "swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_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 "swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_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": "swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF with Ollama:
ollama run hf.co/swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF:Q4_K_M
- Unsloth Studio
How to use swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_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 swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_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 swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF to start chatting
- Docker Model Runner
How to use swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF with Docker Model Runner:
docker model run hf.co/swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF:Q4_K_M
- Lemonade
How to use swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF:Q4_K_M
Run and chat with the model
lemonade run user.LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
"Built with Meta Llama 3".
LLaMAntino-3-ANITA-8B-Inst-DPO-ITA is a model of the LLaMAntino - Large Language Models family. The model is an instruction-tuned version of Meta-Llama-3-8b-instruct (a fine-tuned LLaMA 3 model). This model version aims to be the a Multilingual Model 🏁 (EN 🇺🇸 + ITA🇮🇹) to further fine-tuning on Specific Tasks in Italian.
The 🌟ANITA project🌟 *(Advanced Natural-based interaction for the ITAlian language)* wants to provide Italian NLP researchers with an improved model for the Italian Language 🇮🇹 use cases.
Model Details
https://github.com/marcopoli/LLaMAntino-3-ANITA
- Full Model: LaMAntino-3-ANITA-8B-Inst-DPO-ITA
- LLaMA.cpp - F16 model
- LLaMA.cpp - Q8_0 model
- LLaMA.cpp - Q4_K_M model
- LLaMA.cpp - Q2_K model
Specifications
- Model developers:
Ph.D. Marco Polignano - University of Bari Aldo Moro, Italy
SWAP Research Group - Variations: The model release has been supervised fine-tuning (SFT) using QLoRA 4bit, on instruction-based datasets. DPO approach over the mlabonne/orpo-dpo-mix-40k dataset is used to align with human preferences for helpfulness and safety.
- Input: Models input text only.
- Language: Multilingual 🏁 + Italian 🇮🇹
- Output: Models generate text and code only.
- Model Architecture: Llama 3 architecture.
- Context length: 8K, 8192.
- Library Used: LLaMA.cpp
Prompt Template
<|start_header_id|>system<|end_header_id|>
{ SYS Prompt }<|eot_id|><|start_header_id|>user<|end_header_id|>
{ USER Prompt }<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{ ASSIST Prompt }<|eot_id|>
LLaMA.cpp
LLaMA.cpp, a great tool that helps us easily Quantize your model in GGUF format.
Citation instructions
@misc{polignano2024advanced,
title={Advanced Natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA},
author={Marco Polignano and Pierpaolo Basile and Giovanni Semeraro},
year={2024},
eprint={2405.07101},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{basile2023llamantino,
title={LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language},
author={Pierpaolo Basile and Elio Musacchio and Marco Polignano and Lucia Siciliani and Giuseppe Fiameni and Giovanni Semeraro},
year={2023},
eprint={2312.09993},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
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Model tree for swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF
Base model
meta-llama/Meta-Llama-3-8B-Instruct