metadata
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
Llama3-KALE-LM-Chem 8B
Introduction
We are thrilled to present Llama3-KALE-LM-Chem 8B, the newest version of our Llama3-KALE-LM-Chem model, which embodies nearly half a year of innovation.
Training Details
We have continue pre-trained the model with a large amount of data and post-trained it using supervised fine-tuning.
Benchmarks
Open Benchmarks
| Models | ChemBench | MMLU | MMLU-Chem | SciQ | IE(Acc) | IE(LS) |
|---|---|---|---|---|---|---|
| GPT-3.5 | 47.15 | 69.75 | 53.32 | 89.6 | 52.98 | 68.28 |
| GPT-4 | 53.72 | 78.67 | 63.70 | 94.10 | 54.20 | 69.74 |
| Llama3-8B-Instruct | 46.02 | 68.3 | 51.10 | 93.30 | 45.83 | 61.22 |
| LlaSMol | 28.47 | 54.47 | 33.24 | 72.30 | 2.16 | 3.23 |
| ChemDFM | 44.44 | 58.11 | 45.60 | 86.70 | 7.61 | 11.49 |
| ChemLLM-7B-Chat | 34.16 | 61.79 | 48.39 | 94.00 | 29.66 | 39.17 |
| ChemLLM-7B-Chat-1.5-SFT | 42.75 | 63.56 | 49.63 | 95.10 | 14.96 | 19.61 |
| KALE-LM | 52.40 | 68.74 | 53.83 | 91.50 | 67.50 | 78.37 |
| KALE-LM-INSTRUCT | 57.01 | 68.09 | 54.83 | 91.60 | 57.53 | 64.16 |
In-House Benchmarks
| Models | NC | PP | M2C | C2M | PP | Retro | YP | TP | SP | Average |
|---|---|---|---|---|---|---|---|---|---|---|
| GPT-3.5 | 46.93 | 56.98 | 85.28 | 38.25 | 43.67 | 42.33 | 30.33 | 42.57 | 38 | 47.15 |
| GPT-4 | 54.82 | 65.02 | 92.64 | 52.88 | 62.67 | 52.67 | 42.33 | 24.75 | 35.67 | 53.72 |
| Llama3-8B-Instruct | 51.31 | 27.79 | 90.30 | 40.88 | 34.00 | 30.00 | 45.33 | 60.89 | 33.67 | 46.02 |
| LlaSMol | 27.78 | 29.34 | 31.44 | 23.38 | 25.67 | 24.00 | 37.33 | 34.65 | 22.67 | 28.47 |
| ChemDFM | 36.92 | 55.57 | 83.95 | 42.00 | 40.00 | 37.33 | 39.00 | 33.17 | 32.00 | 44.44 |
| ChemLLM-7B-Chat | 41.05 | 29.76 | 85.28 | 26.12 | 26.00 | 24.00 | 20.00 | 24.26 | 31.00 | 34.16 |
| ChemLLM-7B-Chat-1.5-SFT | 50.06 | 49.51 | 85.28 | 38.75 | 38.00 | 26.67 | 28.33 | 31.68 | 33.67 | 42.44 |
| OURMODEL | 63.58 | 58.39 | 92.98 | 44.50 | 48.67 | 38.33 | 46.33 | 44.55 | 34.33 | 52.41 |
| OURMODELINSTRUCT | 61.33 | 43.44 | 90.30 | 53.62 | 72.67 | 53.67 | 46.00 | 47.03 | 45.00 | 57.01 |
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]
Citation
Will Coming soon....