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from transformers import AutoTokenizer, AutoModel | |
tokenizer = AutoTokenizer.from_pretrained(".\\models\\chatglm-6b-int4", trust_remote_code=True, revision="") | |
model = AutoModel.from_pretrained(".\\models\\chatglm-6b-int4", trust_remote_code=True, revision="").half().cuda() | |
kernel_file = | |
model = model.quantize(bits=4, kernel_file=kernel) | |
model = model.eval() | |
def parse_text(text): | |
lines = text.split("\n") | |
lines = [line for line in lines if line != ""] | |
count = 0 | |
for i, line in enumerate(lines): | |
if "```" in line: | |
count += 1 | |
items = line.split('`') | |
if count % 2 == 1: | |
lines[i] = f'<pre><code class="language-{items[-1]}">' | |
else: | |
lines[i] = f'<br></code></pre>' | |
else: | |
if i > 0: | |
if count % 2 == 1: | |
line = line.replace("`", "\`") | |
line = line.replace("<", "<") | |
line = line.replace(">", ">") | |
line = line.replace(" ", " ") | |
line = line.replace("*", "*") | |
line = line.replace("_", "_") | |
line = line.replace("-", "-") | |
line = line.replace(".", ".") | |
line = line.replace("!", "!") | |
line = line.replace("(", "(") | |
line = line.replace(")", ")") | |
line = line.replace("$", "$") | |
lines[i] = "<br>"+line | |
text = "".join(lines) | |
return text | |
def predict(input, chatbot, max_length, top_p, temperature, history): | |
chatbot.append((parse_text(input), "")) | |
for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p, | |
temperature=temperature): | |
chatbot[-1] = (parse_text(input), parse_text(response)) | |
yield chatbot, history | |
response_new = '' | |
history = [] | |
for chatbot, history in predict('θ―·εδΈη―1000εηζ£ζ', chatbot=[], max_length=10000, top_p=0.5, temperature=0.5, history=history): | |
response_old = response_new | |
response_new = chatbot[0][1] | |
new_single = response_new.replace(response_old, '') | |
print(new_single,end='') | |