├── .github └── workflows │ └── release.yml ├── .gitignore ├── LICENSE ├── README.md ├── benchllama ├── __init__.py ├── __main__.py ├── constants.py ├── data_io │ ├── __init__.py │ └── loader.py ├── evaluation │ ├── __init__.py │ ├── code_runner.py │ ├── evaluator.py │ ├── runners │ │ ├── __init__.py │ │ ├── cpp_runner.py │ │ ├── go_runner.py │ │ ├── java_runner.py │ │ ├── javascript_runner.py │ │ ├── python_runner.py │ │ └── utils.py │ └── score_estimator.py ├── inference │ ├── __init__.py │ ├── model_provider.py │ └── prompt_formatter.py ├── logger.py ├── main.py └── utils.py ├── examples └── custom_js_evals.jsonl ├── media └── benchllama.gif ├── poetry.lock ├── pyproject.toml └── tests └── __init__.py /.github/workflows/release.yml: -------------------------------------------------------------------------------- 1 | name: Publish to PyPI 2 | on: 3 | release: 4 | types: [published] 5 | 6 | jobs: 7 | pypi_release: 8 | name: Builds Using Poetry and Publishes to PyPI 9 | runs-on: ubuntu-latest 10 | steps: 11 | - uses: actions/checkout@v2 12 | - uses: actions/setup-python@v2 13 | - name: Install Poetry 14 | run: curl -sSL https://install.python-poetry.org | python - 15 | - name: Add Poetry to path 16 | run: echo "${HOME}/.poetry/bin" >> $GITHUB_PATH 17 | - run: poetry install 18 | - run: poetry config pypi-token.pypi "${{ secrets.PYPI_API_KEY }}" 19 | - name: Publish package 20 | run: poetry publish --build 21 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Srikanth Srungarapu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |
2 |

🧮 Benchllama

3 |

4 | An open-source tool to benchmark you local LLMs. 5 |

6 | License: MIT 7 | PyPI 8 | Supported Versions 9 | GitHub: Downloads 10 | 11 | Discord 12 | 13 |

14 | 15 |

16 |
17 | 18 | # 🔑 Key points 19 | 20 | Benchllama helps with benchmarking your local LLMs. Currently, it only supports benchmarking models served via Ollama. By default, it pulls [`bigcode/humanevalpack`](https://huggingface.co/datasets/bigcode/humanevalpack) from HuggingFace. There is out-of-box support for evaluating code coding models (you need to use `--eval` flag for triggering this). Currently, it supports the following languages: Python, JavaScript, Java, Go, C++. You can also bring your dataset (see this [example](https://github.com/srikanth235/benchllama/tree/master/examples) to help you with creating one) by specifying the path to it in the `--dataset` flag. 21 | 22 | # 📜 Background 23 | 24 | With the explosion of open source LLMs and toolbox to further customize these models like [Modelfiles](https://github.com/ollama/ollama/blob/main/docs/modelfile.md), [Mergekit](https://github.com/arcee-ai/mergekit), [LoRA](https://github.com/microsoft/LoRA) etc, it can be daunting to end users to choose the right LLM. From our experience with running local LLMs, the two key metrics that matter are performance and quality of responses. We created a simple CLI tool that enables the users to pick the right LLM by evaluating them across these two parameters. 25 | 26 | Given our experience in coding LLMs, we felt it would be useful to add out-of-box support for calculating `pass@k` for coding models. In case, if you are into coding LLMs, please checkout our related project i.e **Privy** ([github repo](https://github.com/srikanth235/privy), [vscode link](https://marketplace.visualstudio.com/items?itemName=privy.privy-vscode), [openvsx link](https://open-vsx.org/extension/Privy/privy-vscode)). 27 | 28 | # ✨ Features 29 | 30 | - **Evaluate**: Evaluate the performance of your models on various tasks, such as code generation. 31 | - **Clean**: Clean up temporary files generated by Benchllama. 32 | 33 | # 🚀 Installation 34 | 35 | ```console 36 | $ pip install benchllama 37 | ``` 38 | 39 | # ⚙️ Usage 40 | 41 | ```console 42 | $ benchllama [OPTIONS] COMMAND [ARGS]... 43 | ``` 44 | 45 | **Options**: 46 | 47 | - `--install-completion`: Install completion for the current shell. 48 | - `--show-completion`: Show completion for the current shell, to copy it or customize the installation. 49 | - `--help`: Show this message and exit. 50 | 51 | **Commands**: 52 | 53 | - `evaluate` 54 | - `clean` 55 | 56 | ## `benchllama evaluate` 57 | 58 | **Usage**: 59 | 60 | ```console 61 | $ benchllama evaluate [OPTIONS] 62 | ``` 63 | 64 | **Options**: 65 | 66 | - `--models TEXT`: Names of models that need to be evaluated. [required] 67 | - `--provider-url TEXT`: The endpoint of the model provider. [default: http://localhost:11434] 68 | - `--dataset FILE`: By default, bigcode/humanevalpack from Hugging Face will be used. If you want to use your own dataset, specify the path here. 69 | - `--languages [python|js|java|go|cpp]`: List of languages to evaluate from bigcode/humanevalpack. Ignore this if you are brining your own data [default: Language.python] 70 | - `--num-completions INTEGER`: Number of completions to be generated for each task. [default: 3] 71 | - `--no-eval / --eval`: If true, evaluation will be done [default: no-eval] 72 | - `--k INTEGER`: The k for calculating pass@k. The values shouldn't exceed num_completions [default: 1, 2] 73 | - `--samples INTEGER`: Number of dataset samples to evaluate. By default, all the samples get processed. [default: -1] 74 | - `--output PATH`: Output directory [default: /tmp] 75 | - `--help`: Show this message and exit. 76 | 77 | ## `benchllama clean` 78 | 79 | **Usage**: 80 | 81 | ```console 82 | $ benchllama clean [OPTIONS] 83 | ``` 84 | 85 | **Options**: 86 | 87 | - `--run-id TEXT`: Run id 88 | - `--output PATH`: Output directory [default: /tmp] 89 | - `--help`: Show this message and exit. 90 | -------------------------------------------------------------------------------- /benchllama/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/srikanth235/benchllama/a826a20f58e7bf326d6cb30ba84561faa2077fc2/benchllama/__init__.py -------------------------------------------------------------------------------- /benchllama/__main__.py: -------------------------------------------------------------------------------- 1 | from .main import app 2 | app(name='benchllama') -------------------------------------------------------------------------------- /benchllama/constants.py: -------------------------------------------------------------------------------- 1 | from enum import Enum 2 | 3 | PROMPT_EVAL_DURATION = "prompt_eval_duration" 4 | PROMPT_EVAL_COUNT = "prompt_eval_count" 5 | EVAL_COUNT = "eval_count" 6 | EVAL_DURATION = "eval_duration" 7 | COMPLETION = "completion" 8 | PROMPT_EVAL_RATE = "prompt_eval_rate" 9 | EVAL_RATE = "eval_rate" 10 | 11 | class Language(str, Enum): 12 | python = "python", 13 | js = "js", 14 | java = "java", 15 | go = "go", 16 | cpp = "cpp" 17 | 18 | 19 | class Result(Enum): 20 | SUCCESS = 1 21 | FAILURE = 0 -------------------------------------------------------------------------------- /benchllama/data_io/__init__.py: -------------------------------------------------------------------------------- 1 | from .loader import Loader 2 | -------------------------------------------------------------------------------- /benchllama/data_io/loader.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | from datasets import load_dataset, concatenate_datasets 4 | from rich.progress import Progress, SpinnerColumn, TextColumn 5 | 6 | 7 | class Loader: 8 | dataset = None 9 | languages = None 10 | 11 | def __init__(self, name, languages=["python"]): 12 | self.languages = languages 13 | with Progress( 14 | SpinnerColumn(), 15 | TextColumn("[progress.description]{task.description}"), 16 | transient=True, 17 | ) as progress: 18 | progress.add_task(description="Loading dataset...", total=None) 19 | if name is None: 20 | self.dataset = concatenate_datasets( 21 | [ 22 | load_dataset( 23 | "bigcode/humanevalpack", language, trust_remote_code=True 24 | )["test"] 25 | for language in languages 26 | ] 27 | ) 28 | else: 29 | self.dataset = load_dataset("json", data_files={"test": str(name)})["test"] 30 | 31 | def get_data(self, models, samples=-1): 32 | df = self.dataset.to_pandas() 33 | df["language"] = df["task_id"].apply(lambda x: x.split("/")[0].lower()) 34 | if samples != -1: 35 | df = df.groupby("language", group_keys=False).apply( 36 | lambda group: group.sample(replace=False, n=min(samples, len(group))) 37 | ) 38 | dfs = [] 39 | for model in models: 40 | curr_df = df.copy() 41 | curr_df["model"] = model 42 | dfs.append(curr_df) 43 | result_df = pd.concat(dfs, ignore_index=True) 44 | return result_df 45 | -------------------------------------------------------------------------------- /benchllama/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from .evaluator import Evaluator 2 | -------------------------------------------------------------------------------- /benchllama/evaluation/code_runner.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from pathlib import Path 3 | 4 | 5 | from ..constants import Result 6 | from .runners.python_runner import PythonRunner 7 | 8 | from .runners.cpp_runner import CppRunner 9 | # from .runners.rust_runner import RustRunner 10 | from .runners.java_runner import JavaRunner 11 | from .runners.javascript_runner import JavascriptRunner 12 | from .runners.go_runner import GoRunner 13 | 14 | 15 | class CodeRunner: 16 | def __init__(self, execution_dir: Path): 17 | self.execution_dir = execution_dir 18 | self.python_runner = PythonRunner(execution_dir) 19 | self.javascript_runner = JavascriptRunner(execution_dir) 20 | self.java_runner = JavaRunner(execution_dir) 21 | self.cpp_runner = CppRunner(execution_dir) 22 | self.go_runner = GoRunner(execution_dir) 23 | 24 | def run(self, problem: pd.Series): 25 | if problem["language"].lower() == "python": 26 | return self.python_runner.run(problem) 27 | elif problem["language"].lower() == "cpp": 28 | return self.cpp_runner.run(problem) 29 | elif problem["language"].lower() == "java": 30 | return self.java_runner.run(problem) 31 | elif problem["language"].lower() == "javascript": 32 | return self.javascript_runner.run(problem) 33 | elif problem["language"].lower() == "go": 34 | return self.go_runner.run(problem) 35 | else: 36 | return Result.FAILURE, "Language not supported!" 37 | -------------------------------------------------------------------------------- /benchllama/evaluation/evaluator.py: -------------------------------------------------------------------------------- 1 | import uuid 2 | import pandas as pd 3 | from typing import List, Optional 4 | from joblib import Parallel, delayed 5 | from rich.progress import ( 6 | BarColumn, 7 | MofNCompleteColumn, 8 | Progress, 9 | TextColumn, 10 | TimeElapsedColumn, 11 | ) 12 | from pathlib import Path 13 | from .score_estimator import ScoreEstimator 14 | from .code_runner import CodeRunner 15 | 16 | 17 | class Evaluator: 18 | def __init__(self, input_df: pd.DataFrame, output: Path): 19 | self.score_estimator = ScoreEstimator() 20 | self.execution_dir = output / "benchllama" / str(uuid.uuid4()) 21 | self.execution_dir.mkdir(parents=True, exist_ok=True) 22 | self.code_runner = CodeRunner(self.execution_dir) 23 | self.input_df = input_df 24 | 25 | def store_raw_data(self): 26 | with open(self.execution_dir / "input_data.jsonl", "w") as f: 27 | f.write(self.input_df.to_json(orient="records", lines=True)) 28 | 29 | def execute_code(self): 30 | outputs = [] 31 | errors = [] 32 | with Progress( 33 | TextColumn(f"• [progress.percentage]" + "{task.percentage:>3.0f}%"), 34 | BarColumn(), 35 | MofNCompleteColumn(), 36 | TextColumn("•"), 37 | TimeElapsedColumn(), 38 | ) as progress: 39 | task = progress.add_task("[cyan]Verifying code...", total=len(self.input_df)) 40 | for result, error in Parallel(n_jobs=-1)( 41 | delayed(self.code_runner.run)(row) for index, row in self.input_df.iterrows() 42 | ): 43 | outputs.append(result.value) 44 | errors.append(error) 45 | progress.update(task, advance=1) 46 | 47 | self.input_df["result"] = outputs 48 | self.input_df["errors"] = errors 49 | return self.input_df 50 | 51 | def estimate_score(self, no_eval: bool, k: Optional[List[int]] = None): 52 | return self.score_estimator.estimate_score(self.input_df, no_eval, k) 53 | 54 | def get_execution_directory(self): 55 | return self.execution_dir 56 | -------------------------------------------------------------------------------- /benchllama/evaluation/runners/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/srikanth235/benchllama/a826a20f58e7bf326d6cb30ba84561faa2077fc2/benchllama/evaluation/runners/__init__.py -------------------------------------------------------------------------------- /benchllama/evaluation/runners/cpp_runner.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import subprocess 3 | 4 | from benchllama.constants import Result 5 | from pathlib import Path 6 | from .utils import get_prompt_and_completion 7 | 8 | class CppRunner: 9 | def __init__(self, execution_dir: Path): 10 | self.execution_dir = execution_dir 11 | 12 | def run(self, problem: pd.Series): 13 | result = Result.FAILURE 14 | error = "" 15 | prompt, completion = get_prompt_and_completion(problem) 16 | code = prompt + completion + "\n" + problem["test"] + "\n" 17 | 18 | dir_path = ( 19 | self.execution_dir 20 | / f"task_{problem.task_id.split('/')[-1]}" 21 | / f"execution_{problem.name}" 22 | ) 23 | dir_path.mkdir(parents=True, exist_ok=True) 24 | 25 | cur_file = dir_path / "test.cpp" 26 | 27 | with open(cur_file, "w") as file: 28 | file.write(code) 29 | 30 | try: 31 | compilation_response = subprocess.run( 32 | ["g++ -std=c++17 test.cpp"], 33 | timeout=5, 34 | cwd=dir_path, 35 | check=True, 36 | capture_output=True, 37 | shell=True, 38 | ) 39 | if compilation_response.returncode != 0: 40 | raise Exception("Compilation failed") 41 | 42 | response = subprocess.run( 43 | ["./a.out"], 44 | cwd=dir_path, 45 | timeout=5, 46 | check=True, 47 | capture_output=True, 48 | shell=True, 49 | ) 50 | if response.returncode == 0: 51 | result = Result.SUCCESS 52 | elif response.stderr: 53 | error = response.stderr.decode() 54 | else: 55 | error = response.stdout.decode() 56 | except Exception as e: 57 | error = str(e) 58 | return result, error 59 | -------------------------------------------------------------------------------- /benchllama/evaluation/runners/go_runner.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import subprocess 3 | import shutil 4 | 5 | from benchllama.constants import Result 6 | from pathlib import Path 7 | from .utils import get_prompt_and_completion 8 | 9 | 10 | class GoRunner: 11 | def __init__(self, execution_dir: Path): 12 | self.execution_dir = execution_dir 13 | self.go_template_dir = None 14 | 15 | def run(self, problem: pd.Series): 16 | if self.go_template_dir is None: 17 | self.go_template_dir = self.execution_dir / "go_module" 18 | self.go_template_dir.mkdir(parents=True, exist_ok=True) 19 | response = subprocess.run( 20 | [ 21 | "go mod init main; go get github.com/stretchr/testify/assert", 22 | ], 23 | cwd=self.go_template_dir, 24 | check=True, 25 | shell=True, 26 | capture_output=True, 27 | ) 28 | 29 | result = Result.FAILURE 30 | error = "" 31 | prompt, completion = get_prompt_and_completion(problem) 32 | code = "package main" + "\n" + prompt + completion 33 | test_code = problem["test_setup"] + problem["test"] 34 | 35 | dir_path = ( 36 | self.execution_dir 37 | / f"task_{problem.task_id.split('/')[-1]}" 38 | / f"execution_{problem.name}" 39 | ) 40 | dir_path.mkdir(parents=True, exist_ok=True) 41 | shutil.copytree(self.go_template_dir, dir_path, dirs_exist_ok=True) 42 | 43 | cur_file = dir_path / "main.go" 44 | cur_test_file = dir_path / "main_test.go" 45 | 46 | # Write the code to a file 47 | with open(cur_file, "w") as file: 48 | file.write(code) 49 | 50 | with open(cur_test_file, "w") as file: 51 | file.write(test_code) 52 | 53 | try: 54 | response = subprocess.run( 55 | ["go test *.go"], 56 | timeout=5, 57 | cwd=dir_path, 58 | check=True, 59 | shell=True, 60 | capture_output=True, 61 | ) 62 | if response.returncode == 0: 63 | result = Result.SUCCESS 64 | elif response.stderr: 65 | error = response.stderr.decode() 66 | elif response.stdout: 67 | error = response.stdout.decode() 68 | except Exception as e: 69 | error = str(e) 70 | return result, error 71 | -------------------------------------------------------------------------------- /benchllama/evaluation/runners/java_runner.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import subprocess 3 | 4 | from benchllama.constants import Result 5 | from pathlib import Path 6 | from .utils import get_prompt_and_completion 7 | 8 | 9 | class JavaRunner: 10 | def __init__(self, execution_dir: Path): 11 | self.execution_dir = execution_dir 12 | 13 | def run(self, problem: pd.Series): 14 | result = Result.FAILURE 15 | error = "" 16 | prompt, completion = get_prompt_and_completion(problem) 17 | code = prompt + completion + "\n" + problem["test"] + "\n" 18 | 19 | dir_path = ( 20 | self.execution_dir 21 | / f"task_{problem.task_id.split('/')[-1]}" 22 | / f"execution_{problem.name}" 23 | ) 24 | 25 | dir_path.mkdir(parents=True, exist_ok=True) 26 | 27 | cur_file = dir_path / "Main.java" 28 | 29 | # Write the code to a file 30 | with open(cur_file, "w") as file: 31 | file.write(code) 32 | 33 | try: 34 | compilation_response = subprocess.run( 35 | ["javac Main.java"], 36 | timeout=5, 37 | cwd=dir_path, 38 | check=True, 39 | shell=True, 40 | capture_output=True, 41 | ) 42 | if compilation_response.returncode != 0: 43 | raise Exception("Compilation failed") 44 | 45 | response = subprocess.run( 46 | ["java Main"], 47 | cwd=dir_path, 48 | timeout=5, 49 | check=True, 50 | capture_output=True, 51 | shell=True, 52 | ) 53 | if response.returncode == 0: 54 | result = Result.SUCCESS 55 | elif response.stderr: 56 | error = response.stderr.decode() 57 | elif response.stdout: 58 | error = response.stdout.decode() 59 | except Exception as e: 60 | error = str(e) 61 | return result, error 62 | -------------------------------------------------------------------------------- /benchllama/evaluation/runners/javascript_runner.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import subprocess 3 | 4 | from benchllama.constants import Result 5 | from pathlib import Path 6 | from .utils import get_prompt_and_completion 7 | 8 | 9 | class JavascriptRunner: 10 | def __init__(self, execution_dir: Path): 11 | self.execution_dir = execution_dir 12 | 13 | def run(self, problem: pd.Series): 14 | result = Result.FAILURE 15 | error = "" 16 | prompt, completion = get_prompt_and_completion(problem) 17 | code = ( 18 | prompt 19 | + completion 20 | + "\n" 21 | + problem["test"] 22 | + "\n" 23 | ) 24 | cur_file = ( 25 | self.execution_dir 26 | / f"task_{problem.task_id.split('/')[-1]}" 27 | / f"execution_{problem.name}.js" 28 | ) 29 | cur_file.parent.mkdir(parents=True, exist_ok=True) 30 | 31 | # Write the code to a file 32 | with open(cur_file, "w") as file: 33 | file.write(code) 34 | 35 | try: 36 | response = subprocess.run( 37 | ["node " + str(cur_file)], 38 | shell=True, 39 | cwd=cur_file.parent, 40 | timeout=5, 41 | check=True, 42 | capture_output=True, 43 | ) 44 | if response.stderr: 45 | error = response.stderr.decode() 46 | elif response.stdout: 47 | error = response.stdout.decode() 48 | else: 49 | result = Result.SUCCESS 50 | except Exception as e: 51 | error = str(e.stderr) 52 | return result, error 53 | -------------------------------------------------------------------------------- /benchllama/evaluation/runners/python_runner.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import subprocess 3 | 4 | from benchllama.constants import Result 5 | from pathlib import Path 6 | from .utils import get_prompt_and_completion 7 | 8 | 9 | class PythonRunner: 10 | def __init__(self, execution_dir: Path): 11 | self.execution_dir = execution_dir 12 | 13 | def run(self, problem: pd.Series): 14 | result = Result.FAILURE 15 | error = "" 16 | prompt, completion = get_prompt_and_completion(problem) 17 | code = ( 18 | prompt 19 | + completion 20 | + "\n" 21 | + problem["test"] 22 | + "\n" 23 | ) 24 | 25 | cur_file = ( 26 | self.execution_dir 27 | / f"task_{problem.task_id.split('/')[-1]}" 28 | / f"execution_{problem.name}.py" 29 | ) 30 | cur_file.parent.mkdir(parents=True, exist_ok=True) 31 | # Write the code to a file 32 | with open(cur_file, "w") as file: 33 | file.write(code) 34 | 35 | # Execute the Python script 36 | try: 37 | response = subprocess.run( 38 | ["python3 " + str(cur_file)], 39 | timeout=5, 40 | check=True, 41 | capture_output=True, 42 | shell=True, 43 | ) 44 | if response.stderr.decode(): 45 | error = response.stderr.decode() 46 | elif response.stdout.decode(): 47 | error = response.stdout.decode() 48 | else: 49 | result = Result.SUCCESS 50 | except Exception as e: 51 | error = str(e.stderr) 52 | return result, error 53 | -------------------------------------------------------------------------------- /benchllama/evaluation/runners/utils.py: -------------------------------------------------------------------------------- 1 | import re 2 | 3 | 4 | def is_instruct_model(row): 5 | return row["instruction"] == row["prompt"] 6 | 7 | 8 | def parse_code(row): 9 | try: 10 | text = row["completion"] 11 | pattern = r"```([\w\s]*)\n([\s\S]+?)```" 12 | snippets = re.findall(pattern, text) 13 | return snippets[0][1] 14 | except Exception: 15 | return "" 16 | 17 | def get_prompt_and_completion(row): 18 | if is_instruct_model(row): 19 | prompt = "" 20 | completion = parse_code(row) 21 | else: 22 | prompt = row["prompt"] 23 | completion = row["completion"] 24 | return prompt, completion -------------------------------------------------------------------------------- /benchllama/evaluation/score_estimator.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | from typing import List, Optional 4 | from ..constants import ( 5 | PROMPT_EVAL_DURATION, 6 | PROMPT_EVAL_COUNT, 7 | EVAL_DURATION, 8 | EVAL_COUNT, 9 | PROMPT_EVAL_RATE, 10 | EVAL_RATE, 11 | ) 12 | 13 | 14 | class ScoreEstimator: 15 | @staticmethod 16 | def pass_at_k(n, c, k): 17 | """ 18 | :param n: total number of samples 19 | :param c: number of correct samples 20 | :param k: k in pass@$k$ 21 | """ 22 | if n - c < k: 23 | return 1.0 24 | return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1)) 25 | 26 | @staticmethod 27 | def model_task_aggregator(df_group, no_eval, k): 28 | n = df_group["result"].count() if no_eval is False else 0 29 | c = df_group["result"].sum() if no_eval is False else 0 30 | passes = {f"pass@{cur_k}": ScoreEstimator.pass_at_k(n, c, cur_k) for cur_k in k} if no_eval is False else {} 31 | return pd.Series( 32 | { 33 | PROMPT_EVAL_DURATION: df_group[PROMPT_EVAL_DURATION].mean(), 34 | PROMPT_EVAL_COUNT: df_group[PROMPT_EVAL_COUNT].mean(), 35 | EVAL_DURATION: df_group[EVAL_DURATION].mean(), 36 | EVAL_COUNT: df_group[EVAL_COUNT].mean(), 37 | **passes, 38 | } 39 | ) 40 | 41 | @staticmethod 42 | def model_aggregator(df_group, no_eval, k): 43 | passes = {f"pass@{cur_k}": df_group[f"pass@{cur_k}"].mean() for cur_k in k} if no_eval is False else {} 44 | return pd.Series( 45 | { 46 | PROMPT_EVAL_DURATION: df_group[PROMPT_EVAL_DURATION].mean() / 10**9, 47 | PROMPT_EVAL_RATE: 10**9 48 | * df_group[PROMPT_EVAL_COUNT].mean() 49 | / df_group[PROMPT_EVAL_DURATION].mean(), 50 | EVAL_DURATION: df_group[EVAL_DURATION].mean() / 10**9, 51 | EVAL_RATE: 10**9 52 | * df_group[EVAL_COUNT].mean() 53 | / df_group[EVAL_DURATION].mean(), 54 | **passes, 55 | } 56 | ) 57 | 58 | def estimate_score(self, input_df: pd.DataFrame, no_eval=True, k: Optional[List[int]] = None): 59 | result_df = ( 60 | input_df.groupby(["model", "task_id", "language"], group_keys=True) 61 | .apply(ScoreEstimator.model_task_aggregator, no_eval=no_eval, k=k) 62 | .reset_index() 63 | .groupby(["model", "language"]) 64 | .apply(ScoreEstimator.model_aggregator, no_eval=no_eval, k=k) 65 | .reset_index() 66 | ) 67 | return result_df 68 | -------------------------------------------------------------------------------- /benchllama/inference/__init__.py: -------------------------------------------------------------------------------- 1 | from .model_provider import ModelProvider 2 | -------------------------------------------------------------------------------- /benchllama/inference/model_provider.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | from ollama import Client 4 | from .prompt_formatter import PromptFormatter 5 | from ..constants import ( 6 | PROMPT_EVAL_DURATION, 7 | PROMPT_EVAL_COUNT, 8 | EVAL_COUNT, 9 | EVAL_DURATION, 10 | COMPLETION, 11 | ) 12 | from rich.progress import ( 13 | BarColumn, 14 | MofNCompleteColumn, 15 | Progress, 16 | TextColumn, 17 | TimeElapsedColumn, 18 | ) 19 | 20 | from rich import print 21 | 22 | 23 | class ModelProvider(object): 24 | def __init__(self, provider_url="http://localhost:11434"): 25 | self.client = Client(provider_url) 26 | self.prompt_formatter = PromptFormatter() 27 | 28 | def run_inference(self, data: pd.DataFrame, num_completions: int) -> pd.DataFrame: 29 | def infer(row): 30 | result = self.client.generate( 31 | model=row["model"], prompt=row["prompt"], options={"stop": row["stop"]} 32 | ) 33 | return ( 34 | result.get("prompt_eval_duration"), 35 | result.get("prompt_eval_count", 0), 36 | result.get("eval_duration"), 37 | result.get("eval_count", 0), 38 | result.get("response"), 39 | ) 40 | 41 | processed_rows = [] 42 | with Progress( 43 | TextColumn(f"• [progress.percentage]" + "{task.percentage:>3.0f}%"), 44 | BarColumn(), 45 | MofNCompleteColumn(), 46 | TextColumn("•"), 47 | TimeElapsedColumn(), 48 | ) as progress: 49 | data = pd.concat( 50 | [data.copy() for _ in range(num_completions)], ignore_index=True 51 | ) 52 | data = data.sort_values(by='model') 53 | # Iterate over the DataFrame rows and process each row 54 | for index, row in progress.track( 55 | data.iterrows(), description="Executing prompts...", total=len(data) 56 | ): 57 | row["prompt"], row["stop"] = self.prompt_formatter.format(row).values() 58 | 59 | result = infer(row) 60 | processed_row = row.copy() 61 | processed_row[PROMPT_EVAL_DURATION] = result[0] 62 | processed_row[PROMPT_EVAL_COUNT] = result[1] 63 | processed_row[EVAL_DURATION] = result[2] 64 | processed_row[EVAL_COUNT] = result[3] 65 | processed_row[COMPLETION] = result[4] 66 | processed_rows.append(processed_row) 67 | 68 | data = pd.DataFrame(processed_rows) 69 | return data 70 | -------------------------------------------------------------------------------- /benchllama/inference/prompt_formatter.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from typing import Dict, Any 3 | 4 | 5 | class PromptFormatter: 6 | def __init__(self): 7 | pass 8 | 9 | def format(self, row: pd.core.series.Series): 10 | data = row.to_dict() 11 | additional_context = data.get("additional_context", "") 12 | prefix = data.get("prompt", "") 13 | suffix = data.get("suffix", "") 14 | model = data.get("model") 15 | 16 | if all([value in model.lower() for value in ["deepseek", "base"]]): 17 | return { 18 | "prompt": f"<｜fim▁begin｜>{additional_context}\n{prefix}<｜fim▁hole｜>{suffix}<｜fim▁end｜>", 19 | "stop": ["<｜fim▁begin｜>", "<｜fim▁hole｜>", "<｜fim▁end｜>", ""], 20 | } 21 | elif "stable" in model.lower() or "starcoder" in model.lower(): 22 | return { 23 | "prompt": f"{additional_context}\n{prefix}{suffix}", 24 | "stop": ["<|endoftext|>"], 25 | } 26 | elif all([value in model.lower() for value in ["codellama", "code"]]): 27 | return { 28 | "prompt": f"
{additional_context}\n{prefix} {suffix} ",
29 |                 "stop": ["
", "", "", "", "EOT"],
30 |             }
31 |         else:
32 |             return {
33 |                 "prompt": data.get("instruction"),
34 |                 "stop": []
35 |             }
36 | 


--------------------------------------------------------------------------------
/benchllama/logger.py:
--------------------------------------------------------------------------------
 1 | import logging
 2 | import logging.config
 3 | 
 4 | from rich.logging import RichHandler
 5 | 
 6 | 
 7 | def setup_logger(log_level=logging.WARNING):
 8 |     logger = logging.getLogger(__name__)
 9 |     logging.basicConfig(
10 |         level=logging.WARNING,
11 |         format="%(message)s",
12 |         datefmt="[%X]",
13 |         handlers=[RichHandler(rich_tracebacks=True)]
14 |     )
15 |     logging.getLogger().setLevel(log_level)
16 |     return logger
17 | 
18 | # Initialize logger
19 | logger = setup_logger()
20 | 


--------------------------------------------------------------------------------
/benchllama/main.py:
--------------------------------------------------------------------------------
  1 | import shutil
  2 | import time
  3 | from pathlib import Path
  4 | from typing import List, Optional
  5 | 
  6 | import typer
  7 | from rich import print
  8 | from typing_extensions import Annotated
  9 | 
 10 | from .constants import Language
 11 | from .data_io import Loader
 12 | from .evaluation import Evaluator
 13 | from .inference import ModelProvider
 14 | from .utils import pretty_print
 15 | 
 16 | app = typer.Typer()
 17 | 
 18 | 
 19 | @app.command()
 20 | def clean(
 21 |     run_id: Annotated[Optional[str], typer.Option(help="Run id")] = None,
 22 |     output: Annotated[
 23 |         Optional[Path],
 24 |         typer.Option(
 25 |             exists=True,
 26 |             dir_okay=True,
 27 |             writable=True,
 28 |             resolve_path=True,
 29 |             help="Output directory",
 30 |         ),
 31 |     ] = "/tmp",
 32 | ):
 33 |     if run_id:
 34 |         directory_path = output / "benchllama" / str(run_id)
 35 |     else:
 36 |         directory_path = output / "benchllama"
 37 | 
 38 |     delete = typer.confirm(f"Are you sure you want to delete {str(directory_path)}?")
 39 | 
 40 |     if not delete:
 41 |         print("Not deleting")
 42 |         raise typer.Abort()
 43 | 
 44 |     try:
 45 |         shutil.rmtree(directory_path)
 46 |         print("Directory removed successfully.")
 47 |     except FileNotFoundError:
 48 |         print("Directory does not exist!")
 49 |     except OSError as e:
 50 |         print(f"Error: {e}")
 51 | 
 52 | 
 53 | @app.command()
 54 | def evaluate(
 55 |     models: Annotated[
 56 |         List[str], typer.Option(help="Names of models that need to be evaluated.")
 57 |     ],
 58 |     provider_url: Annotated[
 59 |         Optional[str], typer.Option(help="The endpoint of the model provider.")
 60 |     ] = "http://localhost:11434",
 61 |     dataset: Annotated[
 62 |         Optional[Path],
 63 |         typer.Option(
 64 |             help="By default, bigcode/humanevalpack from Hugging Face will be used.  If you want to use your own dataset, specify the path here.",
 65 |             exists=True,
 66 |             dir_okay=False,
 67 |             readable=True,
 68 |             resolve_path=True,
 69 |         ),
 70 |     ] = None,
 71 |     languages: Annotated[
 72 |         Optional[List[Language]],
 73 |         typer.Option(
 74 |             help="List of languages to evaluate from bigcode/humanevalpack. Ignore this if you are brining your own data",
 75 |             case_sensitive=False,
 76 |         ),
 77 |     ] = list([Language.python]),
 78 |     num_completions: Annotated[
 79 |         Optional[int],
 80 |         typer.Option(help="Number of completions to be generated for each task."),
 81 |     ] = 1,
 82 |     no_eval: Annotated[
 83 |         bool, typer.Option("--no-eval/--eval", help="If true, evaluation will be done")
 84 |     ] = True,
 85 |     k: Annotated[
 86 |         Optional[List[int]],
 87 |         typer.Option(
 88 |             help="The k for calculating pass@k. The values shouldn't exceed num_completions"
 89 |         ),
 90 |     ] = list([1]),
 91 |     samples: Annotated[
 92 |         Optional[int],
 93 |         typer.Option(
 94 |             help="Number of dataset samples to evaluate. By default, all the samples get processed."
 95 |         ),
 96 |     ] = -1,
 97 |     output: Annotated[
 98 |         Optional[Path],
 99 |         typer.Option(
100 |             help="Output directory",
101 |             exists=True,
102 |             dir_okay=True,
103 |             writable=True,
104 |             resolve_path=True,
105 |         ),
106 |     ] = "/tmp",
107 | ):
108 |     ###### Phase 1 - Loading Data ########
109 |     start_time = time.time()
110 |     input_df = Loader(dataset, languages=languages).get_data(models, samples)
111 |     print(
112 |         f"\n:white_check_mark: Dataset loaded :boom: in { time.time() - start_time :.4f} seconds."
113 |     )
114 | 
115 | 
116 |     ###### Phase 2 - Running Inference ########
117 |     start_time = time.time()
118 |     print(
119 |         "\n:bulb: If inference is taking too long, use [green]--samples[/green] flag to adjust the number of samples.\n"
120 |     )
121 |     result_df = ModelProvider(provider_url).run_inference(input_df, num_completions)
122 |     print(
123 |         f"\n:white_check_mark: Prompts inferred :boom: in { time.time() - start_time :.4f} seconds.\n"
124 |     )
125 | 
126 |     ###### Phase 3 - Evaluation ########
127 |     evaluator = Evaluator(result_df, output)
128 |     evaluator.store_raw_data()
129 |     start_time = time.time()
130 |     if no_eval:
131 |         result_df = evaluator.estimate_score(True)
132 |         print(
133 |             "\n:bulb: For autocompletion based coding LLMs, use [green]--eval[/green] flag to enable pass@k calculations.\n"
134 |         )
135 |     else:
136 |         evaluator.execute_code()
137 |         result_df = evaluator.estimate_score(False, k)
138 |     print(
139 |         f"\n:white_check_mark: Evaluation completed :boom: in { time.time() - start_time :.4f} seconds.\n"
140 |     )
141 | 
142 | 
143 | 
144 |     pretty_print(result_df)
145 |     print(
146 |         "\n:file_folder: You can access the run data at: ",
147 |         evaluator.get_execution_directory(),
148 |     )
149 | 


--------------------------------------------------------------------------------
/benchllama/utils.py:
--------------------------------------------------------------------------------
 1 | import pandas as pd
 2 | from rich.table import Table
 3 | from rich.console import Console
 4 | from .constants import PROMPT_EVAL_DURATION, EVAL_DURATION, PROMPT_EVAL_RATE, EVAL_RATE
 5 | 
 6 | console = Console()
 7 | 
 8 | 
 9 | def pretty_print(df: pd.DataFrame):
10 |     table = Table(title=":fire: Benchmark Results")
11 |     hidden_columns = []
12 |     for column in df.columns:
13 |         if column == "model":
14 |             table.add_column("Model", justify="right", style="yellow", overflow="fold")
15 |         elif column == "language":
16 |             table.add_column("Language", style="purple")
17 |         elif column == PROMPT_EVAL_DURATION:
18 |             table.add_column(
19 |                 "Prompt Eval Duration (in secs)", justify="right", style="green"
20 |             )
21 |         elif column == PROMPT_EVAL_RATE:
22 |             table.add_column(
23 |                 "Prompt Eval Rate (in tokens/sec)", justify="right", style="green"
24 |             )
25 |         elif column == EVAL_DURATION:
26 |             table.add_column("Eval Duration (in secs)", justify="right", style="green")
27 |         elif column == EVAL_RATE:
28 |             table.add_column(
29 |                 "Eval Rate (in tokens/sec)", justify="right", style="green"
30 |             )
31 |         elif "pass@" in column:
32 |             table.add_column(column, justify="right", style="cyan")
33 |         else:
34 |             hidden_columns.append(column)
35 | 
36 |     # # Add rows from DataFrame
37 |     for _, row in df.iterrows():
38 |         table.add_row(
39 |             *[
40 |                 row[column] if isinstance(row[column], str) else f"{row[column]:.3f}"
41 |                 for column in df.columns if column not in hidden_columns
42 |             ]
43 |         )
44 |     console.print(table)
45 | 


--------------------------------------------------------------------------------
/examples/custom_js_evals.jsonl:
--------------------------------------------------------------------------------
1 | {
2 |   "task_id": "JavaScript/0",
3 |   "prompt": "/* Check if in given list of numbers, are any two numbers closer to each other than\n  given threshold.\n  >>> hasCloseElements([1.0, 2.0, 3.0], 0.5)\n  false\n  >>> hasCloseElements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n  true\n  */\nconst hasCloseElements = (numbers, threshold) => {\n",
4 |   "test": "const testHasCloseElements = () => {\n  console.assert(hasCloseElements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) === true)\n  console.assert(\n    hasCloseElements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) === false\n  )\n  console.assert(hasCloseElements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) === true)\n  console.assert(hasCloseElements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) === false)\n  console.assert(hasCloseElements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) === true)\n  console.assert(hasCloseElements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) === true)\n  console.assert(hasCloseElements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) === false)\n}\n\ntestHasCloseElements()\n"
5 | }
6 | 


--------------------------------------------------------------------------------
/media/benchllama.gif:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/srikanth235/benchllama/a826a20f58e7bf326d6cb30ba84561faa2077fc2/media/benchllama.gif


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  88 | 
  89 | [package.dependencies]
  90 | aiosignal = ">=1.1.2"
  91 | async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
  92 | attrs = ">=17.3.0"
  93 | frozenlist = ">=1.1.1"
  94 | multidict = ">=4.5,<7.0"
  95 | yarl = ">=1.0,<2.0"
  96 | 
  97 | [package.extras]
  98 | speedups = ["Brotli", "aiodns", "brotlicffi"]
  99 | 
 100 | [[package]]
 101 | name = "aiosignal"
 102 | version = "1.3.1"
 103 | description = "aiosignal: a list of registered asynchronous callbacks"
 104 | category = "main"
 105 | optional = false
 106 | python-versions = ">=3.7"
 107 | files = [
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 110 | ]
 111 | 
 112 | [package.dependencies]
 113 | frozenlist = ">=1.1.0"
 114 | 
 115 | [[package]]
 116 | name = "anyio"
 117 | version = "4.2.0"
 118 | description = "High level compatibility layer for multiple asynchronous event loop implementations"
 119 | category = "main"
 120 | optional = false
 121 | python-versions = ">=3.8"
 122 | files = [
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 125 | ]
 126 | 
 127 | [package.dependencies]
 128 | exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 129 | idna = ">=2.8"
 130 | sniffio = ">=1.1"
 131 | typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 132 | 
 133 | [package.extras]
 134 | doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
 135 | test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
 136 | trio = ["trio (>=0.23)"]
 137 | 
 138 | [[package]]
 139 | name = "async-timeout"
 140 | version = "4.0.3"
 141 | description = "Timeout context manager for asyncio programs"
 142 | category = "main"
 143 | optional = false
 144 | python-versions = ">=3.7"
 145 | files = [
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 148 | ]
 149 | 
 150 | [[package]]
 151 | name = "attrs"
 152 | version = "23.2.0"
 153 | description = "Classes Without Boilerplate"
 154 | category = "main"
 155 | optional = false
 156 | python-versions = ">=3.7"
 157 | files = [
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 160 | ]
 161 | 
 162 | [package.extras]
 163 | cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
 164 | dev = ["attrs[tests]", "pre-commit"]
 165 | docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
 166 | tests = ["attrs[tests-no-zope]", "zope-interface"]
 167 | tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
 168 | tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 169 | 
 170 | [[package]]
 171 | name = "certifi"
 172 | version = "2024.2.2"
 173 | description = "Python package for providing Mozilla's CA Bundle."
 174 | category = "main"
 175 | optional = false
 176 | python-versions = ">=3.6"
 177 | files = [
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 180 | ]
 181 | 
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 183 | name = "charset-normalizer"
 184 | version = "3.3.2"
 185 | description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 186 | category = "main"
 187 | optional = false
 188 | python-versions = ">=3.7.0"
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 280 | ]
 281 | 
 282 | [[package]]
 283 | name = "click"
 284 | version = "8.1.7"
 285 | description = "Composable command line interface toolkit"
 286 | category = "main"
 287 | optional = false
 288 | python-versions = ">=3.7"
 289 | files = [
 290 |     {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
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 292 | ]
 293 | 
 294 | [package.dependencies]
 295 | colorama = {version = "*", markers = "platform_system == \"Windows\""}
 296 | 
 297 | [[package]]
 298 | name = "colorama"
 299 | version = "0.4.6"
 300 | description = "Cross-platform colored terminal text."
 301 | category = "main"
 302 | optional = false
 303 | python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 304 | files = [
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 307 | ]
 308 | 
 309 | [[package]]
 310 | name = "datasets"
 311 | version = "2.17.0"
 312 | description = "HuggingFace community-driven open-source library of datasets"
 313 | category = "main"
 314 | optional = false
 315 | python-versions = ">=3.8.0"
 316 | files = [
 317 |     {file = "datasets-2.17.0-py3-none-any.whl", hash = "sha256:1479667383d002c2b4a4fc6ac0fb99a7f8e7e440f348991ae7343837f9fc84db"},
 318 |     {file = "datasets-2.17.0.tar.gz", hash = "sha256:81e32e0393a8ca398800223992bfd6222f8a830a6e6aaf3b41b887646f771a86"},
 319 | ]
 320 | 
 321 | [package.dependencies]
 322 | aiohttp = "*"
 323 | dill = ">=0.3.0,<0.3.9"
 324 | filelock = "*"
 325 | fsspec = {version = ">=2023.1.0,<=2023.10.0", extras = ["http"]}
 326 | huggingface-hub = ">=0.19.4"
 327 | multiprocess = "*"
 328 | numpy = ">=1.17"
 329 | packaging = "*"
 330 | pandas = "*"
 331 | pyarrow = ">=12.0.0"
 332 | pyarrow-hotfix = "*"
 333 | pyyaml = ">=5.1"
 334 | requests = ">=2.19.0"
 335 | tqdm = ">=4.62.1"
 336 | xxhash = "*"
 337 | 
 338 | [package.extras]
 339 | apache-beam = ["apache-beam (>=2.26.0)"]
 340 | audio = ["librosa", "soundfile (>=0.12.1)"]
 341 | benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
 342 | dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.1.5)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
 343 | docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
 344 | jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
 345 | metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
 346 | quality = ["ruff (>=0.1.5)"]
 347 | s3 = ["s3fs"]
 348 | tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
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 486 |     {file = "frozenlist-1.4.1.tar.gz", hash = "sha256:c037a86e8513059a2613aaba4d817bb90b9d9b6b69aace3ce9c877e8c8ed402b"},
 487 | ]
 488 | 
 489 | [[package]]
 490 | name = "fsspec"
 491 | version = "2023.10.0"
 492 | description = "File-system specification"
 493 | category = "main"
 494 | optional = false
 495 | python-versions = ">=3.8"
 496 | files = [
 497 |     {file = "fsspec-2023.10.0-py3-none-any.whl", hash = "sha256:346a8f024efeb749d2a5fca7ba8854474b1ff9af7c3faaf636a4548781136529"},
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 499 | ]
 500 | 
 501 | [package.dependencies]
 502 | aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
 503 | requests = {version = "*", optional = true, markers = "extra == \"http\""}
 504 | 
 505 | [package.extras]
 506 | abfs = ["adlfs"]
 507 | adl = ["adlfs"]
 508 | arrow = ["pyarrow (>=1)"]
 509 | dask = ["dask", "distributed"]
 510 | devel = ["pytest", "pytest-cov"]
 511 | dropbox = ["dropbox", "dropboxdrivefs", "requests"]
 512 | full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 513 | fuse = ["fusepy"]
 514 | gcs = ["gcsfs"]
 515 | git = ["pygit2"]
 516 | github = ["requests"]
 517 | gs = ["gcsfs"]
 518 | gui = ["panel"]
 519 | hdfs = ["pyarrow (>=1)"]
 520 | http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
 521 | libarchive = ["libarchive-c"]
 522 | oci = ["ocifs"]
 523 | s3 = ["s3fs"]
 524 | sftp = ["paramiko"]
 525 | smb = ["smbprotocol"]
 526 | ssh = ["paramiko"]
 527 | tqdm = ["tqdm"]
 528 | 
 529 | [[package]]
 530 | name = "h11"
 531 | version = "0.14.0"
 532 | description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 533 | category = "main"
 534 | optional = false
 535 | python-versions = ">=3.7"
 536 | files = [
 537 |     {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
 538 |     {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
 539 | ]
 540 | 
 541 | [[package]]
 542 | name = "httpcore"
 543 | version = "1.0.2"
 544 | description = "A minimal low-level HTTP client."
 545 | category = "main"
 546 | optional = false
 547 | python-versions = ">=3.8"
 548 | files = [
 549 |     {file = "httpcore-1.0.2-py3-none-any.whl", hash = "sha256:096cc05bca73b8e459a1fc3dcf585148f63e534eae4339559c9b8a8d6399acc7"},
 550 |     {file = "httpcore-1.0.2.tar.gz", hash = "sha256:9fc092e4799b26174648e54b74ed5f683132a464e95643b226e00c2ed2fa6535"},
 551 | ]
 552 | 
 553 | [package.dependencies]
 554 | certifi = "*"
 555 | h11 = ">=0.13,<0.15"
 556 | 
 557 | [package.extras]
 558 | asyncio = ["anyio (>=4.0,<5.0)"]
 559 | http2 = ["h2 (>=3,<5)"]
 560 | socks = ["socksio (>=1.0.0,<2.0.0)"]
 561 | trio = ["trio (>=0.22.0,<0.23.0)"]
 562 | 
 563 | [[package]]
 564 | name = "httpx"
 565 | version = "0.25.2"
 566 | description = "The next generation HTTP client."
 567 | category = "main"
 568 | optional = false
 569 | python-versions = ">=3.8"
 570 | files = [
 571 |     {file = "httpx-0.25.2-py3-none-any.whl", hash = "sha256:a05d3d052d9b2dfce0e3896636467f8a5342fb2b902c819428e1ac65413ca118"},
 572 |     {file = "httpx-0.25.2.tar.gz", hash = "sha256:8b8fcaa0c8ea7b05edd69a094e63a2094c4efcb48129fb757361bc423c0ad9e8"},
 573 | ]
 574 | 
 575 | [package.dependencies]
 576 | anyio = "*"
 577 | certifi = "*"
 578 | httpcore = ">=1.0.0,<2.0.0"
 579 | idna = "*"
 580 | sniffio = "*"
 581 | 
 582 | [package.extras]
 583 | brotli = ["brotli", "brotlicffi"]
 584 | cli = ["click (>=8.0.0,<9.0.0)", "pygments (>=2.0.0,<3.0.0)", "rich (>=10,<14)"]
 585 | http2 = ["h2 (>=3,<5)"]
 586 | socks = ["socksio (>=1.0.0,<2.0.0)"]
 587 | 
 588 | [[package]]
 589 | name = "huggingface-hub"
 590 | version = "0.20.3"
 591 | description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 592 | category = "main"
 593 | optional = false
 594 | python-versions = ">=3.8.0"
 595 | files = [
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 598 | ]
 599 | 
 600 | [package.dependencies]
 601 | filelock = "*"
 602 | fsspec = ">=2023.5.0"
 603 | packaging = ">=20.9"
 604 | pyyaml = ">=5.1"
 605 | requests = "*"
 606 | tqdm = ">=4.42.1"
 607 | typing-extensions = ">=3.7.4.3"
 608 | 
 609 | [package.extras]
 610 | all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 611 | cli = ["InquirerPy (==0.3.4)"]
 612 | dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 613 | fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 614 | inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
 615 | quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
 616 | tensorflow = ["graphviz", "pydot", "tensorflow"]
 617 | testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
 618 | torch = ["torch"]
 619 | typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 620 | 
 621 | [[package]]
 622 | name = "idna"
 623 | version = "3.6"
 624 | description = "Internationalized Domain Names in Applications (IDNA)"
 625 | category = "main"
 626 | optional = false
 627 | python-versions = ">=3.5"
 628 | files = [
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 630 |     {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
 631 | ]
 632 | 
 633 | [[package]]
 634 | name = "joblib"
 635 | version = "1.3.2"
 636 | description = "Lightweight pipelining with Python functions"
 637 | category = "main"
 638 | optional = false
 639 | python-versions = ">=3.7"
 640 | files = [
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 643 | ]
 644 | 
 645 | [[package]]
 646 | name = "markdown-it-py"
 647 | version = "3.0.0"
 648 | description = "Python port of markdown-it. Markdown parsing, done right!"
 649 | category = "main"
 650 | optional = false
 651 | python-versions = ">=3.8"
 652 | files = [
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 655 | ]
 656 | 
 657 | [package.dependencies]
 658 | mdurl = ">=0.1,<1.0"
 659 | 
 660 | [package.extras]
 661 | benchmarking = ["psutil", "pytest", "pytest-benchmark"]
 662 | code-style = ["pre-commit (>=3.0,<4.0)"]
 663 | compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
 664 | linkify = ["linkify-it-py (>=1,<3)"]
 665 | plugins = ["mdit-py-plugins"]
 666 | profiling = ["gprof2dot"]
 667 | rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
 668 | testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 669 | 
 670 | [[package]]
 671 | name = "mdurl"
 672 | version = "0.1.2"
 673 | description = "Markdown URL utilities"
 674 | category = "main"
 675 | optional = false
 676 | python-versions = ">=3.7"
 677 | files = [
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 680 | ]
 681 | 
 682 | [[package]]
 683 | name = "multidict"
 684 | version = "6.0.5"
 685 | description = "multidict implementation"
 686 | category = "main"
 687 | optional = false
 688 | python-versions = ">=3.7"
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1400 | 
1401 | [[package]]
1402 | name = "yarl"
1403 | version = "1.9.4"
1404 | description = "Yet another URL library"
1405 | category = "main"
1406 | optional = false
1407 | python-versions = ">=3.7"
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1500 | 
1501 | [package.dependencies]
1502 | idna = ">=2.0"
1503 | multidict = ">=4.0"
1504 | 
1505 | [metadata]
1506 | lock-version = "2.0"
1507 | python-versions = "^3.9"
1508 | content-hash = "69671b2a17d64db2310e4205cf061479a16fe32e2db34333ab41e8024fffa82d"
1509 | 


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/pyproject.toml:
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 1 | [tool.poetry]
 2 | name = "benchllama"
 3 | version = "0.2.7"
 4 | description = "Benchmark your local LLMs."
 5 | authors = ["Srikanth Srungarapu "]
 6 | license = "MIT License"
 7 | homepage = "https://github.com/srikanth235/benchllama/"
 8 | readme = "README.md"
 9 | keywords = [
10 |     "benchmark",
11 |     "llama",
12 |     "ollama",
13 |     "llms",
14 |     "openai",
15 |     "chatgpt",
16 |     "text-generation",
17 | ]
18 | 
19 | [tool.poetry.scripts]
20 | benchllama = "benchllama.main:app"
21 | 
22 | [tool.poetry.dependencies]
23 | python = "^3.9"
24 | typer = {extras = ["all"], version = "^0.9.0"}
25 | ollama = "^0.1.6"
26 | pandas = "^2.2.0"
27 | datasets = "^2.17.0"
28 | joblib = "^1.3.2"
29 | 
30 | [tool.poetry.dev-dependencies]
31 | 
32 | [build-system]
33 | requires = ["poetry-core"]
34 | build-backend = "poetry.core.masonry.api"
35 | 


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/tests/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/srikanth235/benchllama/a826a20f58e7bf326d6cb30ba84561faa2077fc2/tests/__init__.py


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