89 lines
1.7 KiB
Markdown
89 lines
1.7 KiB
Markdown
## Run benchmark
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### Dependencies
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```
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llama_cpp_python 0.2.38
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guidance 0.1.10
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vllm 0.2.7
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outlines 0.0.25
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```
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### Build dataset
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When benchmarking long document information retrieval, run the following command to build the dataset:
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```bash
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pip install wikipedia
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python3 build_dataset.py
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```
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### Benchmark sglang
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Run Llama-7B
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```bash
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python3 -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
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```
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Benchmark Character Generation
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```bash
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python3 bench_sglang.py --mode character
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```
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Benchmark City Information Retrieval
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```bash
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python3 bench_sglang.py --mode city
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```
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### Benchmark Outlines + vLLM
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Run Llama-7B
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```bash
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python3 -m outlines.serve.serve --tokenizer-mode auto --model meta-llama/Llama-2-7b-chat-hf --disable-log-requests --port 21000
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```
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Benchmark Character Generation
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```bash
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python3 bench_other.py --mode character --backend outlines
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```
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Benchmark City Information Retrieval
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```bash
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python3 bench_other.py --mode city --backend outlines
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```
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### Benchmark guidance
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Run Llama-7B and benchmark character generation
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```bash
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python3 bench_other.py --mode character --backend guidance --parallel 1 --n-ctx 4096 --model-path path/to/gguf
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```
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Run Llama-7B and benchmark city information retrieval
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```bash
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python3 bench_other.py --mode city --backend guidance --parallel 1 --n-ctx 4096 --model-path path/to/gguf
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```
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### Benchmark lmql
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Run Llama-7B and benchmark character generation
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```
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python3 bench_other.py --mode character --backend lmql --parallel 1
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```
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Run Llama-7B and benchmark city information retrieval
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```
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python3 bench_other.py --mode city --backend lmql --parallel 1
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```
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