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:
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:
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:
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:
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:
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 |
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