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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 | |