How to use from
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 roleplaiapp/ALIA-40b-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf roleplaiapp/ALIA-40b-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf roleplaiapp/ALIA-40b-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf roleplaiapp/ALIA-40b-Q8_0-GGUF:Q8_0
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 roleplaiapp/ALIA-40b-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf roleplaiapp/ALIA-40b-Q8_0-GGUF:Q8_0
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 roleplaiapp/ALIA-40b-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf roleplaiapp/ALIA-40b-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/roleplaiapp/ALIA-40b-Q8_0-GGUF:Q8_0
Quick Links

roleplaiapp/ALIA-40b-Q8_0-GGUF

Repo: roleplaiapp/ALIA-40b-Q8_0-GGUF
Original Model: ALIA-40b Organization: BSC-LT Quantized File: alia-40b-q8_0.gguf Quantization: GGUF Quantization Method: Q8_0
Use Imatrix: False
Split Model: False

Overview

This is an GGUF Q8_0 quantized version of ALIA-40b.

Quantization By

I often have idle A100 GPUs while building/testing and training the RP app, so I put them to use quantizing models. I hope the community finds these quantizations useful.

Andrew Webby @ RolePlai

Downloads last month
3
GGUF
Model size
40B params
Architecture
llama
Hardware compatibility
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8-bit

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BSC-LT/ALIA-40b
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Datasets used to train roleplaiapp/ALIA-40b-Q8_0-GGUF