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# import gradio as gr
# import torch
# from transformers import AutoModelForCausalLM, AutoTokenizer
# def load_model():
# model = AutoModelForCausalLM.from_pretrained("mattshumer/mistral-8x7b-chat", trust_remote_code=True)
# tok = AutoTokenizer.from_pretrained("mattshumer/mistral-8x7b-chat")
# return model, tok
# def inference(model, tok, PROMPT):
# x = tok.encode(PROMPT, return_tensors="pt").cuda()
# x = model.generate(x, max_new_tokens=512).cpu()
# return tok.batch_decode(x)
# gr.ChatInterface(inference).queue().launch()
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
#tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
#model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1", torch_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained("mattshumer/mistral-8x7b-chat", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("mattshumer/mistral-8x7b-chat")
model = model.to('cuda:0')
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):
history_transformer_format = history + [[message, ""]]
stop = StopOnTokens()
messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]]) #curr_system_message +
for item in history_transformer_format])
# x = tok.encode(PROMPT, return_tensors="pt").cuda()
# x = model.generate(x, max_new_tokens=512).cpu()
# return tok.batch_decode(x)
model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
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).queue().launch()
def predict(message, history):
history_openai_format = []
for human, assistant in history:
history_openai_format.append({"role": "user", "content": human })
history_openai_format.append({"role": "assistant", "content":assistant})
history_openai_format.append({"role": "user", "content": message})
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
messages= history_openai_format,
temperature=1.0,
stream=True
)
partial_message = ""
for chunk in response:
if len(chunk['choices'][0]['delta']) != 0:
partial_message = partial_message + chunk['choices'][0]['delta']['content']
yield partial_message
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