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import os
import gradio as
from transformers import AutoTokenizer, AutoModelForCausalLM
REPO = "teknium/Replit-v1-CodeInstruct-3B"
description = """# <h1 style="text-align: center; color: white;"><span style='color: #F26207;'> Code Generation by Instruction with Replit-v1-CodeInstruct-3B </h1>
<span style="color: white; text-align: center;"> This model is trained on a large amount of code and fine tuned on code-instruct datasets. You can type an instruction in the ### Instruction: section and received code generation.</span>"""
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(REPO, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(REPO, torch_dtype=torch.bfloat16, trust_remote_code=True)
model.to(device)
model.eval()
custom_css = """
.gradio-container {
background-color: #0D1525;
color:white
}
#orange-button {
background: #F26207 !important;
color: white;
}
.cm-gutters{
border: none !important;
}
"""
def post_processing(prompt, completion):
return prompt + completion
def code_generation(prompt, max_new_tokens=128, temperature=0.2, top_p=0.9, eos_token_id=tokenizer.eos_token_id):
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
generated_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, do_sample=True, use_cache=True, temperature=temperature, top_p=top_p, eos_token_id=eos_token_id)
completion = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_spaces=False)
return post_processing(prompt, completion)
demo = gr.Blocks(
css=custom_css
)
with demo:
gr.Markdown(value=description)
with gr.Row():
input_col , settings_col = gr.Column(scale=6), gr.Column(scale=6),
with input_col:
code = gr.Code(lines=28,label='Input', value="### Instruction:\n\n### Response:")
with settings_col:
with gr.Accordion("Generation Settings", open=True):
max_new_tokens= gr.Slider(
minimum=8,
maximum=128,
step=1,
value=48,
label="Max Tokens",
)
temperature = gr.Slider(
minimum=0.1,
maximum=2.5,
step=0.1,
value=0.2,
label="Temperature",
)
with gr.Row():
run = gr.Button(elem_id="orange-button", value="Generate Response")
event = run.click(code_generation, [code, max_new_tokens, temperature], code, api_name="predict")
demo.queue(max_size=40).launch()
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