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import torch
import gradio as gr
from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
model_name_or_path = "TheBloke/WizardCoder-Guanaco-15B-V1.1-GPTQ"
model_basename = "gptq_model-4bit-128g"
use_triton = False
device = "cuda:0" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
model_basename=model_basename,
use_safetensors=True,
trust_remote_code=False,
device=device,
use_triton=use_triton,
quantize_config=None,
cache_dir="models/"
)
"""
To download from a specific branch, use the revision parameter, as in this example:
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
revision="gptq-4bit-32g-actorder_True",
model_basename=model_basename,
use_safetensors=True,
trust_remote_code=False,
device="cuda:0",
quantize_config=None)
"""
def code_gen(text):
# input_ids = tokenizer(text, return_tensors='pt').input_ids.to(device)
# output = model.generate(
# inputs=input_ids, temperature=0.7, max_new_tokens=124)
# print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=124,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.15
)
response = pipe(text)
print(response)
return response[0]['generated_text']
iface = gr.Interface(fn=code_gen,
inputs=gr.inputs.Textbox(
label="Input Source Code"),
outputs="text",
title="Code Generation")
iface.launch()
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