Llama3-KALE-LM-Chem-8B

Llama3-KALE-LM-Chem-8B

Introduction

We are thrilled to present Llama3-KALE-LM-Chem 8B, our first open-source KALE-LM, which specializes in chemistry.

Training Details

We have continually pre-trained the model with a large amount of data and post-trained it through supervised fine-tuning.

Benchmarks

Open Benchmarks

ChemBench Details (Evaluated By OpenCompass)

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-8B",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-8B")

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=2048
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Cite This Work

@article{dai2024kale,
  title={KALE-LM: Unleash The Power Of AI For Science Via Knowledge And Logic Enhanced Large Model},
  author={Dai, Weichen and Chen, Yezeng and Dai, Zijie and Huang, Zhijie and Liu, Yubo and Pan, Yixuan and Song, Baiyang and Zhong, Chengli and Li, Xinhe and Wang, Zeyu and others},
  journal={arXiv preprint arXiv:2409.18695},
  year={2024}
}