|
--- |
|
library_name: transformers |
|
--- |
|
|
|
# Model Card for X-LoRA-Gemma-7b |
|
|
|
X-LoRA-Gemma combines protein, chemical, bio-inspired and mechanics of materials capabilities. We use a set of four LoRA adapters, defined as follows: |
|
|
|
1. Bioinspired materials |
|
2. Mechanics and materials |
|
3. Protein mechanics tasks (featuring generative sequence-to-property and inverse capabilities) |
|
4. Quantum-mechanics based molecular properties QM9 (featuring generative SMILES-to-property and inverse capabilities |
|
|
|
The model has a variety of capabilities, including designing proteins, designing molecules, and property calculations. |
|
|
|
[](https://colab.research.google.com/drive/https://huggingface.co/lamm-mit/x-lora-gemma-7b/resolve/main/X-LoRA-Gemma_Inference.ipynb) |
|
|
|
 |
|
|
|
|
|
```python |
|
import torch |
|
from xlora.xlora_utils import load_model |
|
|
|
XLoRa_model_name = 'lamm-mit/x-lora-gemma-7b' |
|
|
|
model,tokenizer=load_model(model_name = XLoRa_model_name, |
|
device='cuda:0', |
|
use_flash_attention_2=True, |
|
dtype=torch.bfloat16, |
|
) |
|
eos_token_id= tokenizer('<end_of_turn>', add_special_tokens=False, ) ['input_ids'][0] |
|
``` |
|
|
|
```python |
|
def generate_XLoRA_Gemma (system_prompt='You a helpful assistant. You are familiar with materials science. ', |
|
prompt='What is spider silk in the context of bioinspired materials?', |
|
repetition_penalty=1.,num_beams=1,num_return_sequences=1, |
|
top_p=0.9, top_k=256, temperature=.5,max_new_tokens=512, verbatim=False, eos_token=None, |
|
add_special_tokens=True, prepend_response='', |
|
): |
|
if eos_token==None: |
|
eos_token= tokenizer.eos_token_id |
|
|
|
if system_prompt==None: |
|
messages=[ {"role": "user", "content": prompt}, ] |
|
else: |
|
messages=[ {"role": "user", "content": system_prompt+prompt}, ] |
|
txt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, ) |
|
txt=txt+prepend_response |
|
|
|
inputs = tokenizer(txt, add_special_tokens =add_special_tokens, return_tensors ='pt').to(device) |
|
with torch.no_grad(): |
|
|
|
outputs = model.generate(input_ids = inputs["input_ids"], |
|
attention_mask = inputs["attention_mask"] , # This is usually done automatically by the tokenizer |
|
max_new_tokens=max_new_tokens, |
|
temperature=temperature, #value used to modulate the next token probabilities. |
|
num_beams=num_beams, |
|
top_k = top_k, |
|
top_p = top_p, |
|
num_return_sequences = num_return_sequences, |
|
eos_token_id=eos_token, |
|
pad_token_id = eos_token, |
|
do_sample =True,#skip_prompt=True, |
|
repetition_penalty=repetition_penalty, |
|
) |
|
return tokenizer.batch_decode(outputs[:,inputs["input_ids"].shape[1]:].detach().cpu().numpy(), skip_special_tokens=True) |
|
|
|
``` |
|
Then, use as follows: |
|
```python |
|
from IPython.display import display, Markdown |
|
q='''What is graphene?''' |
|
res=generate_XLoRA_Gemma( system_prompt='You design materials.', prompt=q, max_new_tokens=1024, temperature=0.3, eos_token=eos_token_id) |
|
display (Markdown(res)) |
|
``` |
|
|
|
### Example: Molecular design |
|
|
|
```python |
|
def design_from_target( |
|
model, |
|
tokenizer, |
|
target, |
|
temperature=0.1, |
|
num_beams=1, |
|
top_k=50, |
|
top_p=0.95, |
|
repetition_penalty=1.0, |
|
messages=[] |
|
): |
|
# Format the target line for molecular property generation |
|
line = f'GenerateMolecularProperties<{return_str(target)}>' |
|
|
|
# Add the line to the message history |
|
messages.append({"role": "user", "content": line}) |
|
|
|
# Apply chat template with optional tokenization |
|
line = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
|
|
|
# Generate response with specified parameters |
|
result = generate_response( |
|
model, |
|
tokenizer, |
|
text_input=line, |
|
num_return_sequences=1, |
|
temperature=temperature, |
|
top_k=top_k, |
|
top_p=top_p, |
|
max_new_tokens=256 |
|
)[0] |
|
|
|
return result |
|
``` |
|
Use case: |
|
```python |
|
import numpy as np |
|
target = np.random.rand(12) |
|
SMILES=design_from_target (model, tokenizer, target, messages=[]]) |
|
print (SMILES) |
|
``` |
|
|
|
Calculate molecular properties: |
|
|
|
```python |
|
def properties_from_SMILES( |
|
model, |
|
tokenizer, |
|
target, |
|
temperature=0.1, |
|
top_k=128, |
|
top_p=0.9, |
|
num_beams=1, |
|
repetition_penalty=1.0 |
|
): |
|
# Format the target line for molecular property calculation |
|
line = f'CalculateMolecularProperties<{target}>' |
|
|
|
# Initialize messages and add the formatted line |
|
messages = [{"role": "user", "content": line}] |
|
|
|
# Apply chat template with optional tokenization |
|
line = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
|
|
|
# Generate response with specified parameters |
|
result = generate_response( |
|
model, |
|
tokenizer, |
|
text_input=line, |
|
num_return_sequences=1, |
|
temperature=temperature, |
|
top_k=top_k, |
|
top_p=top_p, |
|
max_new_tokens=256 |
|
)[0] |
|
|
|
# Extract relevant part of the result and convert to float list |
|
result = extract_start_and_end(result, start_token='[', end_token=']') |
|
return [float(i) for i in result.split(',')] |
|
``` |
|
|
|
|
|
 |
|
|
