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 gameversellc/hermes_3_tests_gv:Q4_0
# Run inference directly in the terminal:
llama-cli -hf gameversellc/hermes_3_tests_gv:Q4_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf gameversellc/hermes_3_tests_gv:Q4_0
# Run inference directly in the terminal:
llama-cli -hf gameversellc/hermes_3_tests_gv:Q4_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 gameversellc/hermes_3_tests_gv:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf gameversellc/hermes_3_tests_gv:Q4_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 gameversellc/hermes_3_tests_gv:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gameversellc/hermes_3_tests_gv:Q4_0
Use Docker
docker model run hf.co/gameversellc/hermes_3_tests_gv:Q4_0
Quick Links

Hermes-3-Llama-3.2-3B Q4_0 GGUF

Quantized GGUF version of NousResearch/Hermes-3-Llama-3.2-3B

Model Details

  • Base Model: NousResearch/Hermes-3-Llama-3.2-3B
  • Quantization: Q4_0 (4-bit)
  • Format: GGUF
  • Size: 1.79 GB
  • Use Case: Efficient inference with llama.cpp

Usage

With llama.cpp

# Download model
huggingface-cli download gameversellc/hermes_3_tests_gv Hermes-3-Llama-3.2-3B_q4_0.gguf

# Run inference
./llama-cli -m Hermes-3-Llama-3.2-3B_q4_0.gguf -p "Your prompt here" -n 100

With llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="Hermes-3-Llama-3.2-3B_q4_0.gguf")
output = llm("Your prompt here", max_tokens=100)
print(output)

Performance

  • MMLU Accuracy: ~40% (quantized)
  • Inference Speed: Fast on CPU
  • Memory Usage: ~2 GB RAM

License

Apache 2.0 (same as base model)

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GGUF
Model size
3B params
Architecture
llama
Hardware compatibility
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4-bit

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