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Model: SciReason/SciReasoner-8B
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---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- qwen
- scientific-reasoning
---
# SciReasoner 8B: Laying the Scientific Reasoning Ground Across Disciplines
[![arXiv](https://img.shields.io/badge/arXiv-2509.21320-b31b1b.svg)](https://arxiv.org/abs/2509.21320)
[![Hugging Face](https://img.shields.io/badge/HuggingFace-SciReason-FFAE1A)](https://huggingface.co/SciReason)
[![License](https://img.shields.io/badge/License-Apache_2.0-2D7DB1.svg)](https://www.apache.org/licenses/LICENSE-2.0)
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](https://huggingface.co/papers/2509.21320).
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](https://huggingface.co/SciReason).
Please ensure your environment has internet access.
### Evaluate all datasets
```bash
opencompass examples_scireasoner/eval_all.py --max-num-worker 1
```
* **Default model:** [SciReasoner-8B](https://huggingface.co/SciReason/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`)
```bash
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](https://opencompass.readthedocs.io/en/latest/).
Got it! Ill add a **FAQ section** with the issue and solution clearly explained. Heres 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:**
1. Set the environment variable `HF_HOME` to a **shared/public directory** for Hugging Face cache.
2. On a node with internet access, run a dummy model once to pre-cache everything:
```bash
opencompass examples_scireasoner/eval_all_debug.py --max-num-worker 16
```
3. 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:
```bash
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](https://opencompass.readthedocs.io/en/latest/get_started/quick_start.html).
### 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](https://github.com/open-compass/opencompass/tree/0.4.2) 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](https://opencompass.readthedocs.io/en/latest/).
---
## 📜 License
This project is licensed under the [Apache License 2.0](https://www.apache.org/licenses/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](LICENSE) file for full details.

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special_tokens_map.json Normal file
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tokenizer_config.json Normal file
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1
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