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 Desktop
- 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
- Atomic Chat
Change where mulmat_perf is run
Browse files- bench-TriLMs.py +3 -1
bench-TriLMs.py
CHANGED
|
@@ -169,24 +169,26 @@ if __name__ == "__main__":
|
|
| 169 |
MODEL_DIR = args.model_dir
|
| 170 |
|
| 171 |
results = []
|
|
|
|
| 172 |
repetitions: int = args.repetitions
|
| 173 |
|
| 174 |
if args.cpu:
|
| 175 |
gather_models()
|
| 176 |
build_llama_cpp(["-DGGML_NATIVE=ON", "-DGGML_CPU=ON"])
|
| 177 |
quantize()
|
|
|
|
| 178 |
results.extend(llama_bench(repetitions=repetitions))
|
| 179 |
|
| 180 |
if args.gpu:
|
| 181 |
gather_models()
|
| 182 |
build_llama_cpp(["-DGGML_NATIVE=ON", "-DGGML_CUDA=ON", "-DGGML_CUDA_F16=ON"])
|
| 183 |
quantize()
|
|
|
|
| 184 |
results.extend(llama_bench(repetitions=repetitions, types=GPU_TYPES))
|
| 185 |
|
| 186 |
cpuinfo = subprocess.run(["lscpu"], capture_output=True).stdout.decode(
|
| 187 |
encoding="utf-8"
|
| 188 |
)
|
| 189 |
-
mulmat_perf = test_backend_perf()
|
| 190 |
|
| 191 |
final_result = {
|
| 192 |
"cpuinfo": cpuinfo,
|
|
|
|
| 169 |
MODEL_DIR = args.model_dir
|
| 170 |
|
| 171 |
results = []
|
| 172 |
+
mulmat_perf = []
|
| 173 |
repetitions: int = args.repetitions
|
| 174 |
|
| 175 |
if args.cpu:
|
| 176 |
gather_models()
|
| 177 |
build_llama_cpp(["-DGGML_NATIVE=ON", "-DGGML_CPU=ON"])
|
| 178 |
quantize()
|
| 179 |
+
mulmat_perf.append(test_backend_perf())
|
| 180 |
results.extend(llama_bench(repetitions=repetitions))
|
| 181 |
|
| 182 |
if args.gpu:
|
| 183 |
gather_models()
|
| 184 |
build_llama_cpp(["-DGGML_NATIVE=ON", "-DGGML_CUDA=ON", "-DGGML_CUDA_F16=ON"])
|
| 185 |
quantize()
|
| 186 |
+
mulmat_perf.append(test_backend_perf())
|
| 187 |
results.extend(llama_bench(repetitions=repetitions, types=GPU_TYPES))
|
| 188 |
|
| 189 |
cpuinfo = subprocess.run(["lscpu"], capture_output=True).stdout.decode(
|
| 190 |
encoding="utf-8"
|
| 191 |
)
|
|
|
|
| 192 |
|
| 193 |
final_result = {
|
| 194 |
"cpuinfo": cpuinfo,
|