How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wazuki/EpsteinFilez-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf wazuki/EpsteinFilez-8B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wazuki/EpsteinFilez-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf wazuki/EpsteinFilez-8B-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 wazuki/EpsteinFilez-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf wazuki/EpsteinFilez-8B-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 wazuki/EpsteinFilez-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wazuki/EpsteinFilez-8B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/wazuki/EpsteinFilez-8B-GGUF:Q4_K_M
Quick Links

EpsteinFilez-8B-GGUF

An 8B parameter GGUF model built on the Qwen3 architecture. Optimized for local inference with Q4_K_M quantization.

Available Quantizations

File Quant Size
EpsteinFilez-8B-Q4_K_M.gguf Q4_K_M ~4.7 GB

Usage

Works with any GGUF-compatible runtime:

# llama.cpp
./llama-cli -m EpsteinFilez-8B-Q4_K_M.gguf -p "Your prompt here"

# Ollama
ollama create epsteinfilez -f Modelfile
ollama run epsteinfilez

# LM Studio โ€” just drag and drop the .gguf file

Details

  • Architecture: Qwen3
  • Parameters: 8B
  • Format: GGUF (Q4_K_M)
  • License: Apache 2.0
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GGUF
Model size
8B params
Architecture
qwen3
Hardware compatibility
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4-bit

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