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import os
import urllib.request
import gradio as gr
from llama_cpp import Llama
from langchain.llms import llamacpp
from huggingface_hub import login, hf_hub_download
from dotenv import load_dotenv
MODEL_ID = "TheBloke/Llama-2-7b-Chat-GGUF"
MODEL_BASENAME = "llama-2-7b-chat.Q4_K_M.gguf"
# MODEL_ID = "TheBloke/Wizard-Vicuna-7B-Uncensored-GGUF"
# MODEL_BASENAME = "Wizard-Vicuna-7B-Uncensored.Q4_K_M.gguf"
CONTEXT_WINDOW_SIZE = 8000
MAX_NEW_TOKENS = 2000
N_BATCH = 128
# load_dotenv()
os.getenv('hf_token')
def load_quantized_model(model_id, model_basename):
try:
model_path = hf_hub_download(
repo_id=model_id,
filename=model_basename,
resume_download=True,
cache_dir="./models"
)
kwargs = {
'model_path': model_path,
'c_ctx': CONTEXT_WINDOW_SIZE,
'max_tokens': MAX_NEW_TOKENS,
'n_batch': N_BATCH
}
return llamacpp.LlamaCpp(**kwargs)
except TypeError:
return None
def load_model(model_id, model_basename=None):
if ".gguf" in model_basename.lower():
llm = load_quantized_model(model_id, model_basename)
return llm
else:
print("currently only .gguf models supported")
def generate_text(prompt="Who is the CEO of Apple?"):
llm = load_model(MODEL_ID, MODEL_BASENAME)
output = llm(
prompt,
max_tokens=256,
temperature=0.1,
top_p=0.5,
echo=False,
stop=["#"],
)
print(output)
return output
# output_text = output["choices"][0]["text"].strip()
# # Remove Prompt Echo from Generated Text
# cleaned_output_text = output_text.replace(prompt, "")
# return cleaned_output_text
description = "Zephyr-beta"
examples = [
["What is the capital of France?", "The capital of France is Paris."],
[
"Who wrote the novel 'Pride and Prejudice'?",
"The novel 'Pride and Prejudice' was written by Jane Austen.",
],
["What is the square root of 64?", "The square root of 64 is 8."],
]
gradio_interface = gr.Interface(
fn=generate_text,
inputs="text",
outputs="text",
examples=examples,
title="Zephyr-B",
)
gradio_interface.launch(share=True) |