Instructions to use compilade/quant-tests 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 compilade/quant-tests 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 compilade/quant-tests:F16 # Run inference directly in the terminal: llama cli -hf compilade/quant-tests:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf compilade/quant-tests:F16 # Run inference directly in the terminal: llama cli -hf compilade/quant-tests:F16
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 compilade/quant-tests:F16 # Run inference directly in the terminal: ./llama-cli -hf compilade/quant-tests:F16
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 compilade/quant-tests:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf compilade/quant-tests:F16
Use Docker
docker model run hf.co/compilade/quant-tests:F16
- LM Studio
- Jan
- Ollama
How to use compilade/quant-tests with Ollama:
ollama run hf.co/compilade/quant-tests:F16
- Unsloth Studio
How to use compilade/quant-tests with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for compilade/quant-tests to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for compilade/quant-tests to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for compilade/quant-tests to start chatting
- Atomic Chat new
- Docker Model Runner
How to use compilade/quant-tests with Docker Model Runner:
docker model run hf.co/compilade/quant-tests:F16
- Lemonade
How to use compilade/quant-tests with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull compilade/quant-tests:F16
Run and chat with the model
lemonade run user.quant-tests-F16
List all available models
lemonade list
Ask before overwriting the output file
Browse files- bench-TriLMs.py +11 -2
bench-TriLMs.py
CHANGED
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@@ -170,6 +170,7 @@ def parse_args(args: Sequence[str]):
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default=Path(os.path.curdir) / "result.json",
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help="Path of the benchmark results to be written",
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)
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return parser.parse_args(args[1:])
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@@ -181,6 +182,14 @@ if __name__ == "__main__":
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LLAMA_CPP_PATH = args.llama_cpp_path
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MODEL_DIR = args.model_dir
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results = []
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mulmat_perf = []
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repetitions: int = args.repetitions
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"mulmat_perf": mulmat_perf,
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"results": results,
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}
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-
logger.info("Writing output to: %s",
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logger.debug("Final results: %s", json.dumps(final_result, indent=4))
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-
with open(
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json.dump(final_result, f, indent=4)
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f.flush()
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default=Path(os.path.curdir) / "result.json",
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help="Path of the benchmark results to be written",
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)
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parser.add_argument("--force", action="store_true", help="Overwrite the result file without asking")
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return parser.parse_args(args[1:])
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LLAMA_CPP_PATH = args.llama_cpp_path
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MODEL_DIR = args.model_dir
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output_file = Path(args.out).absolute()
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if output_file.exists() and not args.force:
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ask = input("Result file exists. Do you want to overwrite it? [y/N]")
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if not ask.strip().lower().startswith("y"):
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logger.info("Not running, leaving output file intact")
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exit()
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results = []
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mulmat_perf = []
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repetitions: int = args.repetitions
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"mulmat_perf": mulmat_perf,
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"results": results,
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}
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logger.info("Writing output to: %s", output_file)
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logger.debug("Final results: %s", json.dumps(final_result, indent=4))
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with open(output_file, "w") as f:
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json.dump(final_result, f, indent=4)
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f.flush()
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