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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
import torch
model_name = "mistralai/Mistral-7B-Instruct-v0.2"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
low_cpu_mem_usage=True,
# load_in_4bit = True,
quantization_config = bnb_config
)
def generate_text(messages):
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
no_token_encodeds = tokenizer.apply_chat_template(messages, tokenize=False).replace('<s>', "").replace('</s>', "")
output = model.generate(
encodeds,
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
return output_text[len(no_token_encodeds) + 2:]
# # Remove Prompt Echo from Generated Text
# cleaned_output_text = output_text.replace(input_text, "")
# return cleaned_output_text
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