Text Generation
Russian
conversational
saiga_30b_ggml / interact.py
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import fire
from llama_cpp import Llama
MESSAGE_TEMPLATE = "{role}\n{content}"
SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
BOT_TOKEN = 9225
LINEBREAK_TOKEN = 13
def get_message_tokens(model, role, content):
message_text = MESSAGE_TEMPLATE.format(role=role, content=content)
message_tokens = model.tokenize(message_text.encode("utf-8"))
message_tokens.append(model.token_eos())
return message_tokens
def get_system_tokens(model):
system_message = {
"role": "system",
"content": SYSTEM_PROMPT
}
return get_message_tokens(model, **system_message)
def interact(
model_path,
n_ctx=2000,
top_k=30,
top_p=0.9,
temperature=0.2,
repeat_penalty=1.1
):
model = Llama(
model_path=model_path,
n_ctx=n_ctx,
n_parts=1,
)
system_tokens = get_system_tokens(model)
tokens = system_tokens
model.eval(tokens)
while True:
user_message = input("User: ")
message_tokens = get_message_tokens(model=model, role="user", content=user_message)
role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]
tokens += message_tokens + role_tokens
generator = model.generate(
tokens,
top_k=top_k,
top_p=top_p,
temp=temperature,
repeat_penalty=repeat_penalty
)
for token in generator:
token_str = model.detokenize([token]).decode("utf-8")
tokens.append(token)
if token == model.token_eos():
break
print(token_str, end="", flush=True)
print()
if __name__ == "__main__":
fire.Fire(interact)