| Name | llm-evaluation-in-reasoning JSON |
| Version |
1.4.2
JSON |
| download |
| home_page | None |
| Summary | A project for evaluating reasoning capabilities in large language models (LLMs). |
| upload_time | 2025-01-17 07:13:34 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT License Copyright (c) 2024 Sheng Ren and Xiya Pang Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
evaluation
llm
reasoning
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# llm_evaluation_in_reasoning
[](https://github.com/ashengstd/llm_evaluation_in_reasoning) [](https://pypi.org/project/llm_evaluation_in_reasoning) [](https://pypi.org/project/llm_evaluation_in_reasoning/) [](https://pypi.org/project/llm_evaluation_in_reasoning/) [](https://github.com/ashengstd/llm_evaluation_in_reasoning) [](https://github.com/ashengstd/llm_evaluation_in_reasoning)
A project for evaluating reasoning capabilities in large language models (LLMs).
**Read this in other languages: [English](https://github.com/ashengstd/llm_evaluation_in_reasoning/blob/main/README.md), [中文](https://github.com/ashengstd/llm_evaluation_in_reasoning/blob/main/README_zh.md).**
## Run the eval
### Install the package
```shell
pip install llm_evaluation_in_reasoning
```
### Create the `.env` file
Create a `.env` file with the following:
```
OPENAI_API_KEY=<your key>
ANTHROPIC_API_KEY=<your key>
...
```
The api key you provided will be used to fetch the valid models supported by `Litellm`.
### Run Instructions
Support `GSM-Symbolic`, `GSM8K`, `MMLU`, `SimpleBench`
To run a benchmark:
```shell
llm_eval --model_name=ollama/qwen2.5:0.5b --dataset=SimpleBench # run llm_eval --help to see help information
```
## Model support
Model support is based on `Litellm`, see the docs here [Litellm Providers](https://docs.litellm.ai/docs/providers)
## Build the project
### Setup Instructions
Clone the github repo and cd into it.
```shell
git clone https://github.com/ashengstd/llm_evaluation_in_reasoning.git
cd llm_evaluation_in_reasoning
```
### Install uv:
The best way to install dependencies is to use `uv`.
If you don't have it installed in your environment, you can install it with the following:
```shell
curl -LsSf https://astral.sh/uv/install.sh | sh # macOS and Linux
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" # Windows
```
### Sync the dependencies
```shell
uv sync --all-extra
```
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"description": "# llm_evaluation_in_reasoning\n\n[](https://github.com/ashengstd/llm_evaluation_in_reasoning) [](https://pypi.org/project/llm_evaluation_in_reasoning) [](https://pypi.org/project/llm_evaluation_in_reasoning/) [](https://pypi.org/project/llm_evaluation_in_reasoning/) [](https://github.com/ashengstd/llm_evaluation_in_reasoning) [](https://github.com/ashengstd/llm_evaluation_in_reasoning)\n\nA project for evaluating reasoning capabilities in large language models (LLMs).\n\n**Read this in other languages: [English](https://github.com/ashengstd/llm_evaluation_in_reasoning/blob/main/README.md), [\u4e2d\u6587](https://github.com/ashengstd/llm_evaluation_in_reasoning/blob/main/README_zh.md).**\n\n## Run the eval\n\n### Install the package\n\n```shell\npip install llm_evaluation_in_reasoning\n```\n\n### Create the `.env` file\n\nCreate a `.env` file with the following:\n\n```\nOPENAI_API_KEY=<your key>\nANTHROPIC_API_KEY=<your key>\n...\n```\n\nThe api key you provided will be used to fetch the valid models supported by `Litellm`.\n\n### Run Instructions\n\nSupport `GSM-Symbolic`, `GSM8K`, `MMLU`, `SimpleBench`\nTo run a benchmark:\n\n```shell\nllm_eval --model_name=ollama/qwen2.5:0.5b --dataset=SimpleBench # run llm_eval --help to see help information\n```\n\n## Model support\n\nModel support is based on `Litellm`, see the docs here [Litellm Providers](https://docs.litellm.ai/docs/providers)\n\n## Build the project\n\n### Setup Instructions\n\nClone the github repo and cd into it.\n\n```shell\ngit clone https://github.com/ashengstd/llm_evaluation_in_reasoning.git\ncd llm_evaluation_in_reasoning\n```\n\n### Install uv:\n\nThe best way to install dependencies is to use `uv`.\nIf you don't have it installed in your environment, you can install it with the following:\n\n```shell\ncurl -LsSf https://astral.sh/uv/install.sh | sh # macOS and Linux\npowershell -ExecutionPolicy ByPass -c \"irm https://astral.sh/uv/install.ps1 | iex\" # Windows\n```\n\n### Sync the dependencies\n\n```shell\nuv sync --all-extra\n```\n",
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