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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
tokenizer = AutoTokenizer.from_pretrained("IlyaGusev/saiga_llama3_8b")
model = AutoModelForCausalLM.from_pretrained("IlyaGusev/saiga_llama3_8b", torch_dtype=torch.bfloat16)
model = model #.to('cuda')
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [29, 0]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
def predict(message, history):
print(history)
history_transformer_format = history + [{"role": "user", "content": message},
{"role": "assistant", "content": ""}]
stop = StopOnTokens()
# messages = "".join(["".join(["<|start_header_id|>user<|end_header_id|>\n"+item[0],
# "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n"+item[1]])
# for item in history_transformer_format])
# messages = [{"role": "user", item[0], "content": item[1]} for item in history_transformer_format]
#print(messages)
# model_inputs = tokenizer([messages], return_tensors="pt") # .to("cuda")
model_inputs = tokenizer.apply_chat_template(history_transformer_format, return_tensors="pt")
streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=1024,
do_sample=True,
top_p=0.95,
top_k=1000,
temperature=1.0,
num_beams=1,
stopping_criteria=StoppingCriteriaList([stop])
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
partial_message = ""
for new_token in streamer:
if new_token != '<':
partial_message += new_token
yield partial_message
gr.ChatInterface(predict).launch(share=True)
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