Text Generation
GGUF
rth_tcn
code-generation
non-transformer
tcn
fractal
lora
genome
rth-code
zetagrid
Instructions to use RthItalia/Rth-lm-code-25b 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 RthItalia/Rth-lm-code-25b 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 RthItalia/Rth-lm-code-25b # Run inference directly in the terminal: llama cli -hf RthItalia/Rth-lm-code-25b
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RthItalia/Rth-lm-code-25b # Run inference directly in the terminal: llama cli -hf RthItalia/Rth-lm-code-25b
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 RthItalia/Rth-lm-code-25b # Run inference directly in the terminal: ./llama-cli -hf RthItalia/Rth-lm-code-25b
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 RthItalia/Rth-lm-code-25b # Run inference directly in the terminal: ./build/bin/llama-cli -hf RthItalia/Rth-lm-code-25b
Use Docker
docker model run hf.co/RthItalia/Rth-lm-code-25b
- LM Studio
- Jan
- vLLM
How to use RthItalia/Rth-lm-code-25b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RthItalia/Rth-lm-code-25b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RthItalia/Rth-lm-code-25b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RthItalia/Rth-lm-code-25b
- Ollama
How to use RthItalia/Rth-lm-code-25b with Ollama:
ollama run hf.co/RthItalia/Rth-lm-code-25b
- Unsloth Desktop
- Docker Model Runner
How to use RthItalia/Rth-lm-code-25b with Docker Model Runner:
docker model run hf.co/RthItalia/Rth-lm-code-25b
- Lemonade
How to use RthItalia/Rth-lm-code-25b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RthItalia/Rth-lm-code-25b
Run and chat with the model
lemonade run user.Rth-lm-code-25b-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Update README.md
Browse files
README.md
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## 📜 Licenza &
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**CC BY-NC 4.0** — Ricerca e uso personale libero.
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Prodotto da **RTH Italia** (Research & Technology Hub).
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Autore: *Christian Quintino De Luca*.
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Per citare il paper originale:
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📖 **[RTH-LM: A Fractal Temporal Convolutional Language Model](https://zenodo.org/records/18622610)**
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---
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*Costruito per dimostrare che l'efficienza batte la forza bruta.*
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---
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## 📜 Licenza & Uso Commerciale ⚠️
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> **ATTENZIONE: QUESTO MODELLO NON È OPEN SOURCE COMPLETO.**
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> È rilasciato sotto licenza **CC BY-NC 4.0 (Creative Commons Non-Commercial)**.
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### ✅ Cosa PUOI fare (Gratis):
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- Ricerca accademica e personale.
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- Test e valutazione locale.
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- Uso hobbyistico e no-profit.
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- Condividere i risultati citando l'autore.
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### ❌ Cosa NON PUOI fare (Senza Licenza Commerciale):
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- **Usare il modello in azienda** per qualsiasi scopo (interno o esterno).
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- Integrare il modello in prodotti o servizi a pagamento.
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- Offrire API o servizi cloud basati su questo modello.
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- Qualsiasi attività che generi revenue diretta o indiretta.
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📞 **PER USO COMMERCIALE (Enterprise / Startup):**
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Devi ottenere una licenza commerciale da **RTH Italia**.
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Contatto diretto: [**info@rthitalia.com**](mailto:info@rthitalia.com)
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---
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## 📄 Citazione
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Prodotto da **RTH Italia** (Research & Technology Hub).
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Autore: *Christian Quintino De Luca*.
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Per citare il paper originale:
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📖 **[RTH-LM: A Fractal Temporal Convolutional Language Model](https://zenodo.org/records/18622610)**
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```bibtex
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@techreport{deluca2026rthlm,
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author = {De Luca, Christian Quintino},
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title = {RTH-LM: A Fractal Temporal Convolutional Language Model},
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institution = {RTH Italia (Research & Technology Hub)},
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year = {2026},
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url = {https://github.com/rthgit/ZetaGrid},
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doi = {10.5281/zenodo.18622610},
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note = {Non-commercial license. Contact RTH Italia for commercial use.}
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}
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```
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---
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*Costruito per dimostrare che l'efficienza batte la forza bruta.*
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