chatglm-6b-int4 / demo_single_chat.py
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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("<", "&lt;")
line = line.replace(">", "&gt;")
line = line.replace(" ", "&nbsp;")
line = line.replace("*", "&ast;")
line = line.replace("_", "&lowbar;")
line = line.replace("-", "&#45;")
line = line.replace(".", "&#46;")
line = line.replace("!", "&#33;")
line = line.replace("(", "&#40;")
line = line.replace(")", "&#41;")
line = line.replace("$", "&#36;")
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('请写一篇1000ε­—ηš„ζ•£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='')