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import os | |
from threading import Thread | |
from typing import Iterator | |
import gradio as gr | |
import spaces | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
MAX_MAX_NEW_TOKENS = 1024 | |
DEFAULT_MAX_NEW_TOKENS = 512 | |
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) | |
DESCRIPTION = """\ | |
# Llama-2 7B Chat | |
This Space demonstrates model [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta, a Llama 2 model with 7B parameters fine-tuned for chat instructions. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints). | |
🔎 For more details about the Llama 2 family of models and how to use them with `transformers`, take a look [at our blog post](https://huggingface.co/blog/llama2). | |
🔨 Looking for an even more powerful model? Check out the [13B version](https://huggingface.co/spaces/huggingface-projects/llama-2-13b-chat) or the large [70B model demo](https://huggingface.co/spaces/ysharma/Explore_llamav2_with_TGI). | |
""" | |
LICENSE = """ | |
<p/> | |
--- | |
As a derivate work of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta, | |
this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/USE_POLICY.md). | |
""" | |
SYSTEM_PROMPT = "நீங்கள் உதவிகரமான மற்றும் மரியாதைக்குரிய மற்றும் நேர்மையான AI உதவியாளர்." | |
PROMPT_TEMPLATE = """## Instructions:\n{% if messages[0]['role'] == 'system' %}{{ messages[0]['content'] + '\n' }}{% endif %}{% for message in messages %}{% if message['role'] == 'user' %}{{ '\n## Input:\n' + message['content'] + '\n'}}{% elif message['role'] == 'assistant' %}{{ '\n## Response:\n' + message['content'] + '\n'}}{% endif %}{% endfor %}\n\n## Response:\n""" | |
if not torch.cuda.is_available(): | |
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
if torch.cuda.is_available(): | |
model_id = "abhinand/tamil-llama-7b-instruct-v0.1" | |
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") | |
tokenizer = AutoTokenizer.from_pretrained(model_id) | |
tokenizer.chat_template = PROMPT_TEMPLATE | |
tokenizer.use_default_system_prompt = False | |
def generate( | |
message: str, | |
chat_history: list[tuple[str, str]], | |
system_prompt: str = SYSTEM_PROMPT, | |
max_new_tokens: int = 1024, | |
temperature: float = 0.6, | |
top_p: float = 0.9, | |
top_k: int = 50, | |
repetition_penalty: float = 1.2, | |
) -> Iterator[str]: | |
conversation = [] | |
if system_prompt: | |
conversation.append({"role": "system", "content": system_prompt}) | |
for user, assistant in chat_history: | |
conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) | |
conversation.append({"role": "user", "content": message}) | |
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt") | |
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: | |
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] | |
gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") | |
input_ids = input_ids.to(model.device) | |
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) | |
generate_kwargs = dict( | |
{"input_ids": input_ids}, | |
streamer=streamer, | |
max_new_tokens=max_new_tokens, | |
do_sample=True, | |
top_p=top_p, | |
top_k=top_k, | |
temperature=temperature, | |
num_beams=1, | |
repetition_penalty=repetition_penalty, | |
) | |
t = Thread(target=model.generate, kwargs=generate_kwargs) | |
t.start() | |
outputs = [] | |
for text in streamer: | |
outputs.append(text) | |
yield "".join(outputs) | |
chat_interface = gr.ChatInterface( | |
fn=generate, | |
additional_inputs=[ | |
gr.Textbox(label="System prompt", lines=6), | |
gr.Slider( | |
label="Max new tokens", | |
minimum=1, | |
maximum=MAX_MAX_NEW_TOKENS, | |
step=1, | |
value=DEFAULT_MAX_NEW_TOKENS, | |
), | |
gr.Slider( | |
label="Temperature", | |
minimum=0.1, | |
maximum=4.0, | |
step=0.1, | |
value=0.6, | |
), | |
gr.Slider( | |
label="Top-p (nucleus sampling)", | |
minimum=0.05, | |
maximum=1.0, | |
step=0.05, | |
value=0.9, | |
), | |
gr.Slider( | |
label="Top-k", | |
minimum=1, | |
maximum=1000, | |
step=1, | |
value=50, | |
), | |
gr.Slider( | |
label="Repetition penalty", | |
minimum=1.0, | |
maximum=2.0, | |
step=0.05, | |
value=1.2, | |
), | |
], | |
stop_btn=None, | |
examples=[ | |
["வணக்கம், நீங்கள் யார்?"], | |
["நான் பெரிய பணக்காரன் இல்லை, லேட்டஸ்ட் iPhone-இல் நிறைய பணம் செலவழிக்க வேண்டுமா?"], | |
["பட்டியலை வரிசைப்படுத்த பைதான் செயல்பாட்டை எழுதவும்."], | |
["சிவப்பும் மஞ்சளும் கலந்தால் என்ன நிறமாக இருக்கும்?"], | |
["விரைவாக தூங்குவது எப்படி?"], | |
], | |
) | |
with gr.Blocks(css="style.css") as demo: | |
gr.Markdown(DESCRIPTION) | |
gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button") | |
chat_interface.render() | |
gr.Markdown(LICENSE) | |
if __name__ == "__main__": | |
demo.queue(max_size=20).launch() | |