Instructions to use aitups/ALIA-40b-saor 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 aitups/ALIA-40b-saor 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 aitups/ALIA-40b-saor # Run inference directly in the terminal: llama cli -hf aitups/ALIA-40b-saor
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf aitups/ALIA-40b-saor # Run inference directly in the terminal: llama cli -hf aitups/ALIA-40b-saor
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 aitups/ALIA-40b-saor # Run inference directly in the terminal: ./llama-cli -hf aitups/ALIA-40b-saor
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 aitups/ALIA-40b-saor # Run inference directly in the terminal: ./build/bin/llama-cli -hf aitups/ALIA-40b-saor
Use Docker
docker model run hf.co/aitups/ALIA-40b-saor
- LM Studio
- Jan
- Ollama
How to use aitups/ALIA-40b-saor with Ollama:
ollama run hf.co/aitups/ALIA-40b-saor
- Unsloth Desktop
- Docker Model Runner
How to use aitups/ALIA-40b-saor with Docker Model Runner:
docker model run hf.co/aitups/ALIA-40b-saor
- Lemonade
How to use aitups/ALIA-40b-saor with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aitups/ALIA-40b-saor
Run and chat with the model
lemonade run user.ALIA-40b-saor-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
| license: cc-by-sa-4.0 | |
| base_model: mradermacher/ALIA-40b-GGUF | |
| tags: | |
| - saor | |
| - sparse | |
| - gguf | |
| - opencl | |
| # ALIA-40b-saor | |
| Modelo **ALIA-40b** con los bloques FFN podados según la **topología CPPN** del | |
| loop evolutivo de SAOR (Vía B): un genoma CPPN de 466 floats con coordenada de | |
| capa decodifica la adyacencia binaria de cada capa, y los pesos activos se | |
| re-empaquetan en **Q4_K** (formato GGUF disperso **D16**). Modelo base: arquitectura | |
| llama densa (48 bloques FFN `[8192 → 24576]`). | |
| | Métrica | Valor | | |
| |---|---| | |
| | Divergencia KL (vs base, n_pos=4) | **0.723** | | |
| | Compresión D_arch | **0.018** (modelo completo) | | |
| | Capas esparsas | 48 | | |
| | Método de poda | Topología CPPN binaria (Vía B) | | |
| > **Nota sobre el método:** ALIA-40b es **~100× más sensible** a la poda del FFN | |
| > que Qwen3.8-27B: la poda por magnitud uniforme "explota" (KL 2.15 a sp 0.2), y | |
| > ni siquiera la topología CPPN supera KL 0.723 a compresiones mayores. Este | |
| > modelo es el mejor resultado SAOR para ALIA-40b: **1.8 % de compresión | |
| > arquitectónica con KL 0.723**. Para comparar métodos en los otros modelos del | |
| > proyecto, ver Qwen3.8-27B-saor (magnitud + perfil CPPN) y Qwen3.5-4B-saor | |
| > (magnitud + perfil CPPN). | |
| ## Ejecutar con [Hayai](https://github.com/hayai-org/hayai) | |
| ```bash | |
| # 1. Compilar Hayai | |
| cd hayai && cargo build --release | |
| # 2. Dry-run del plan | |
| cargo run --release -p hayai-cli -- plan --model ALIA-40b-saor.gguf | |
| # 3. Generar texto (streaming por capas — 40b no cabe en VRAM pequeña) | |
| cargo run --release -p hayai-cli -- generate \ | |
| --model ALIA-40b-saor.gguf \ | |
| --prompt "The capital of Spain is" \ | |
| --max-tokens 64 \ | |
| --device auto | |
| ``` | |
| ### Evaluar la divergencia KL | |
| ```bash | |
| cargo run --release --example kl_eval -- \ | |
| --orig ALIA-40b.Q4_K_M.gguf \ | |
| --sparse ALIA-40b-saor.gguf \ | |
| --prompts calib128.txt --n-positions 4 --device auto | |
| ``` | |
| ## Archivos | |
| - `ALIA-40b-saor.gguf` — el modelo disperso (~25 GB, Q4_K). | |
| - Genoma CPPN (466 f32) disponible bajo demanda. | |
| ## Reproducibilidad | |
| Topología CPPN global (Vía B) del loop evolutivo de SAOR (`via_b_evolve | |
| --batch-eval`, 4 generaciones). La adyacencia se decodifica en GPU | |
| (`embed_sparse --genome --gpu`). Genoma (466 f32) e histórico disponibles bajo | |
| demanda. | |
| **Atribución**: modelo base `mradermacher/ALIA-40b-GGUF` (BSC-LT) — consulte la | |
| licencia del modelo base. | |