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x-lora-gemma-7b

lamm-mit / x-lora-gemma-7b

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Model's Last Updated: October 25 2024

Introduction of x-lora-gemma-7b

Model Details of x-lora-gemma-7b

Model Card for X-LoRA-Gemma-7b 

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]

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: 

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))
Model Details
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https://huggingface.co/lamm-mit/x-lora-gemma-7b

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