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SciReason/
SciReasoner-8B

Name: SciReasoner-8B
Availability: InStock

text-generation-inference

endpoints_compatible

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SciReasoner 8B: Laying the Scientific Reasoning Ground Across Disciplines

arXiv

License

This repository contains the weight of SciReasoner-8B, a scientific reasoning foundation model. It was presented in the paper SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines.

Code: https://github.com/open-sciencelab/SciReason

Usage:

🔧 Environment Setup

bash

git clone https://github.com/open-sciencelab/SciReason.git
cd SciReason
conda create --name scireason python=3.10 -y
conda activate scireason
pip install -r requirements/training.txt
pip install -e .

Note: The above instructions are for reference only. You may need to adjust them depending on your operating system and environment.

🚀 Running Evaluation

The evaluation script will automatically download the required datasets and models from Hugging Face. Please ensure your environment has internet access.

Evaluate all datasets

opencompass examples_scireasoner/eval_all.py --max-num-worker 1

Default model: SciReasoner-8B
You can replace it with your own model if needed.
The --max-num-worker option controls concurrency:
- By default, each process uses one GPU.
- Adjust it according to your available GPUs.

Evaluate few-shot performance (e.g., for closed-source models like `o3`)

opencompass examples_scireasoner/eval_all_fewshot.py --max-num-worker 1

This script evaluates the few-shot capabilities of your model on all datasets.

Evaluate specific datasets or custom models

To evaluate specific datasets: Modify the configuration file to set datasets as a list of the datasets you want to test.
To use custom models: Modify the configuration file to set models to your target model.
- Reference format: opencompass.configs.models.scireason.hf_scireasoner_8b
- For more model configuration options, please check the OpenCompass documentation.

Got it! I’ll add a FAQ section with the issue and solution clearly explained. Here’s how it fits into your README:

❓ FAQ

1. `meteor_score` Error

If you encounter an error related to meteor_score, you may need to download NLTK resources.

Solution: In an environment with internet access, run:

python

import nltk
nltk.download('wordnet')

By default, the files are downloaded to /root/nltk_data. If you are using a conda environment and running on a compute node or container, download them into your conda environment instead:

python

import nltk
import os

conda_path = os.path.join(os.environ["CONDA_PREFIX"], "nltk_data")
nltk.download('wordnet', download_dir=conda_path)

You can check all search paths using:

python

import nltk
print(nltk.data.path)

2. Running on compute nodes without internet access

If your compute node cannot access the internet due to security policies, you need to pre-download/cache the datasets and models on a node with internet access first.

Recommended steps:

Set the environment variable HF_HOME to a shared/public directory for Hugging Face cache.
On a node with internet access, run a dummy model once to pre-cache everything:

opencompass examples_scireasoner/eval_all_debug.py --max-num-worker 16
Now, you can run the actual evaluation code on the compute node without needing internet access.

3. Resuming from checkpoints & step-wise evaluation

Because the datasets are large and evaluation can be time-consuming, OpenCompass supports resuming from checkpoints and running evaluations in separate stages.

To resume from a checkpoint, use the -r flag with the timestamp of the previous run:

opencompass examples_scireasoner/eval_all.py -r <timestamp>
To run specific stages only, use the --mode flag with one of the following options:
- all – Run the full pipeline (default)
- infer – Run inference only
- eval – Run evaluation only
- viz – Run visualization only

For more details, please refer to the OpenCompass Quick Start Guide.

4. Dataset size cache issue

If you only want to test a subset of a dataset by modifying the code to trim it, be aware that OpenCompass caches the dataset size.

Before running the evaluation, it is recommended to either:

Delete the entire cache file:

rm .cache/dataset_size.json

Or remove the corresponding line for the modified dataset from the cache file.

This ensures that OpenCompass recalculates the dataset size correctly.

🏗️ Codebase and References

This repository is built on top of OpenCompass v0.4.2 with custom modifications. We plan to merge the changes back into the main OpenCompass branch in the future.

For more usage details, please refer to the OpenCompass documentation.

📜 License

This project is licensed under the Apache License 2.0.

You are free to use, modify, and distribute this project under the terms of the Apache 2.0 license.
See the LICENSE file for full details.

SciReason/SciReasoner-8B

SciReasoner 8B: Laying the Scientific Reasoning Ground Across Disciplines

Usage:

🔧 Environment Setup

🚀 Running Evaluation

Evaluate all datasets

Evaluate few-shot performance (e.g., for closed-source models like o3)

Evaluate specific datasets or custom models

❓ FAQ

1. meteor_score Error

2. Running on compute nodes without internet access

3. Resuming from checkpoints & step-wise evaluation

4. Dataset size cache issue

🏗️ Codebase and References

📜 License

SciReason/
SciReasoner-8B

Evaluate few-shot performance (e.g., for closed-source models like `o3`)

1. `meteor_score` Error