Spaces:
Runtime error
Runtime error
File size: 4,826 Bytes
fc1301c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
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 = 256
MAX_INPUT_TOKEN_LENGTH = 512
DESCRIPTION = """\
# OpenELM-3B
This Space demonstrates [OpenELM-3B](apple/OpenELM-3B) by Apple. Please, check the original model card for details.
You can see the other models of the OpenELM family [here](https://huggingface.co/apple/OpenELM)
The following Colab notebooks are available:
* [OpenELM-3B (GPU)](https://gist.github.com/Norod/4f11bb36bea5c548d18f10f9d7ec09b0)
* [OpenELM-270M (CPU)](https://gist.github.com/Norod/5a311a8e0a774b5c35919913545b7af4)
You might also be intrested in checking out Apple's [CoreNet Github page](https://github.com/apple/corenet?tab=readme-ov-file).
If you duplicate this space, make sure you have access to [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
because this model uses it as a tokenizer.
Note: While the user interface is of a chatbot for convenience, this model is the base
model and is not fine-tuned for chatbot tasks or instruction following tasks. As such,
the model is not provided a chat history and will generate text based on the last given prompt.
"""
LICENSE = """
<p/>
---
As a derivate work of [OpenELM-3B](apple/OpenELM-3B) by Apple,
this demo is governed by the original [license](https://huggingface.co/apple/OpenELM-3B/blob/main/LICENSE).
"""
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 = "apple/OpenELM-3B"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, low_cpu_mem_usage=True)
tokenizer_id = "meta-llama/Llama-2-7b-hf"
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
if tokenizer.pad_token == None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
@spaces.GPU
def generate(
message: str,
chat_history: list[tuple[str, str]],
max_new_tokens: int = 1024,
temperature: float = 0.6,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.4,
) -> Iterator[str]:
input_ids = tokenizer([message], return_tensors="pt").input_ids
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.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=1.4,
),
],
stop_btn=None,
examples=[
["A recepie for a chocolate cake:"],
["Can you explain briefly to me what is the Python programming language?"],
["Explain the plot of Cinderella in a sentence."],
["Question: What is the capital of France?\nAnswer:"],
["Question: I am very tired, what should I do?\nAnswer:"],
],
)
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()
|