---
license: agpl-3.0
language:
- en
- zh
tags:
- AI4S
- MoE
---

# SciDFM: Dialogue Foundation Model for Science

SciDFM is the pioneering open-sourced dialogue foundation model tailored for science, which integrates a mixture-of-experts architecture into a transformer-based framework, aiming at enhancing its sophisticated scientific reasoning and understanding capabilities. SciDFM achieves strong performance on general scientific benchmarks such as SciEval and SciQ, and it reachs a SOTA performance on domain-specific benchmark among models of similar size.

## News
* **2024-06-28** The parameter of SciDFM-MoE-A5.6B-v1.0 is open-soursed! Technical report is coming soon.

## Model Details

SciDFM is based on a transformer architecture, and follows modifications of Llama, i.e. RMSNorm, RoPE and SwiGLU. SciDFM use the same hyper-parameters of OpenLLaMa-3B. And in order to better model knowledge of different disciplines, we replace the feed-forward block with Mixture-of-Expert (MoE) layers.

## Training Details

SciDFM is pre-trained on a large corpus containing ~300B science tokens and ~270B general tokens for two epochs, resulting in about 1.1T tokens consuming. And we further fine-tune SciDFM using ~9.3M instruction-following samples for 5 epochs to improve the performances on the downstream benchmarks.

## Usage Details

### Local Inference

To load and run SciDFM locally, here is an example:

```python
import torch
from transformers import LlamaTokenizer, AutoModelForCausalLM

model_name_or_id = "OpenDFM/SciDFM-MoE-A5.6B-v1.0"
tokenizer = LlamaTokenizer.from_pretrained(model_name_or_id, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name_or_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)

chat_template = "<|user|>:{instruction}<|assistant|>:"
input_text = "What is Mixture-of-Experts (MoE) in computer science?"
input_text = chat_template.format(instruction=input_text)

inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
generation_config = GenerationConfig(
    do_sample=True,
    top_k=20,
    top_p=0.9,
    temperature=0.9,
    max_new_tokens=1024,
    eos_token_id=tokenizer.eos_token_id
)

outputs = model.generate(**inputs, generation_config=generation_config)
generated_text = tokenizer.decode(outputs, skip_special_tokens=True)[0][len(input_text):]
print(generated_text.strip())
```

### SMILES preprocess

When there involves SMILES notation in your input, we recommend to preprocess the SMILES with the `rdkit` package to canonicalize the SMILES. Here is an example:
```python
from rdkit import Chem
def canonicalize_smiles(smiles):
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        return None
    return Chem.MolToSmiles(mol, isomericSmiles=True, kekuleSmiles=False)
```
or directly:
```python
from rdkit import Chem
def canonicalize_smiles(smiles):
    return Chem.CanonSmiles(smiles, useChiral=True)
```

### Special Tokens preprocess

If there is SMILES expression in your input, please first process it with the following function:

```python
import sentencepiece as spm

smiles_model = spm.SentencePieceProcessor(model_file="smiles.model")

def convert_smiles(smiles_str):
   pieces = smiles_model.encode_as_pieces(smiles_str)[1:]
   smiles = "".join([f"[ChemDFM_Start_SMILES_Unit]{piece}[ChemDFM_End_SMILES_Unit]" for piece in pieces])
   return smiles

convert_smiles("C(C(=O)O)N")
```

And if there is protein sequece in your input, please first process it with the following function:

```python
def convert_protein(p_str):
   res = [f"<<protein>>{s}" for s in p_str]
   return "".join(res)

convert_protein("MIRLGAPQTL")
```

## Evaluation

We briefly compare SciDFM-MoE-A5.6B-v1.0 with similar-sized instruction-tuned LLMs on scientific evaluation benchmarks. The results are shown below:
| Model              | SciEval | SciQ  | ARC\_c | ARC\_e | GSM8K | MATH  | MedQA | MMCQA | PMQA  | Avg   |
|--------------------|---------|-------|--------|--------|-------|-------|-------|-------|-------|-------|
| LLaMa2-7B          | 27.06   | 57.00 | 36.43  | 46.59  | 3.94  | 3.96  | 26.32 | 29.84 | 66.80 | 32.95 |
| Galactica-6.7B     | 46.28   | 74.20 | 44.28  | 61.83  | 2.80  | 6.32  | 30.48 | 36.46 | 48.80 | 38.91 |
| LLaMa2-13B         | 33.88   | 78.10 | 56.66  | 72.35  | 22.82 | 3.90  | 32.68 | 34.28 | 77.80 | 45.45 |
| ChatGLM2-6B        | 54.25   | 75.80 | 57.08  | 73.57  | 25.09 | 7.18  | 27.42 | 34.21 | 60.40 | 45.94 |
| Galactica-30B      | 54.24   | 83.10 | 57.85  | 75.04  | 13.65 | 8.66  | 37.71 | 48.43 | 58.80 | 48.35 |
| LLaMa3-8B          | 59.70   | 90.00 | 71.16  | 84.05  | 5.91  | 7.00  | 48.78 | 52.74 | 26.60 | 49.59 |
| ChatGLM3-6B        | 51.13   | 77.60 | 60.84  | 75.97  | 60.27 | 23.52 | 24.59 | 31.39 | 51.80 | 50.53 |
| SciGLM-6B          | 61.22   | 88.70 | 77.47  | 86.57  | 42.23 | 16.40 | 42.81 | 44.94 | 73.60 | 59.12 |
| SciDFM             | 62.48   | 88.00 | 64.76  | 81.48  | 59.14 | 27.28 | 44.54 | 53.10 | 78.00 | 61.56 |
| ChatGLM3-6B-base   | 60.34   | 89.00 | 78.58  | 87.37  | 59.82 | 22.64 | 42.73 | 45.14 | 74.40 | 61.96 |
| Llama3-8B-Instruct | 64.91   | 91.60 | 76.45  | 87.33  | 76.57 | 26.26 | 56.48 | 59.31 | 72.00 | 67.44 |

## Citation

```
@article{sun2024scidfm,
  title={SciDFM: A Large Language Model with Mixture-of-Experts for Science},
  author={Sun, Liangtai and Luo, Danyu and Ma, Da and Zhao, Zihan and Chen, Baocai and Shen, Zhennan and Zhu, Su and Chen, Lu and Chen, Xin and Yu, Kai},
  journal={arXiv preprint arXiv:2409.18412},
  year={2024}
}
```