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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)
quantize_config = BaseQuantizeConfig(
bits=4, # quantize model to 4-bit
group_size=128, # it is recommended to set the value to 128
desc_act=False, # set to False can significantly speed up inference but the perplexity may slightly bad
)
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=quantize_config,
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):
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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