inference/sglang/benchmark/json_jump_forward/README.md

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