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
Add more GPU information in the results file
Browse files- bench-TriLMs.py +13 -6
bench-TriLMs.py
CHANGED
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@@ -6,6 +6,7 @@ from pathlib import Path
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from urllib import request
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import os
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import shlex
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import subprocess
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import sys
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from typing import Any, Sequence
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mulmat_perf.append(test_backend_perf())
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results.extend(llama_bench(repetitions=repetitions, types=GPU_TYPES))
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encoding="utf-8"
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)
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final_result = {
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"cpuinfo": cpuinfo,
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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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from urllib import request
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import os
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import shlex
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import shutil
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import subprocess
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import sys
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from typing import Any, Sequence
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mulmat_perf.append(test_backend_perf())
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results.extend(llama_bench(repetitions=repetitions, types=GPU_TYPES))
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final_result: dict[str, Any] = {
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"mulmat_perf": mulmat_perf,
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"results": results,
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}
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if shutil.which("lscpu") is not None:
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logger.info("Getting CPU info")
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final_result["cpuinfo"] = subprocess.run(["lscpu"], capture_output=True).stdout.decode(
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encoding="utf-8"
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)
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if args.gpu and shutil.which("nvidia-smi") is not None:
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logger.info("Getting NVIDIA GPU info")
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final_result["gpuinfo"] = subprocess.run(["nvidia-smi", "-q"], capture_output=True).stdout.decode(encoding="utf-8")
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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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