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Description

This model is a 10.2 billion parameter model that combines two sets of 24 layers each from CALM2-7B-chat.

Chat Template

USER: {user_message1}
ASSISTANT: {assistant_message1}<|endoftext|>
USER: {user_message2}
ASSISTANT: {assistant_message2}<|endoftext|>
USER: {user_message3}
ASSISTANT: {assistant_message3}<|endoftext|>

Tutorial

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("sudy-super/baku-10b-chat")
model = AutoModelForCausalLM.from_pretrained("sudy-super/baku-10b-chat", device_map="auto", torch_dtype=torch.bfloat16)

raw_prompt = "ไป•ไบ‹ใฎ็†ฑๆ„ใ‚’ๅ–ใ‚Šๆˆปใ™ใŸใ‚ใฎใ‚ขใ‚คใƒ‡ใ‚ขใ‚’5ใคๆŒ™ใ’ใฆใใ ใ•ใ„ใ€‚"
prompt = f"USER:{raw_prompt}\nASSISTANT:"

token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=100,
        do_sample=True,
        temperature=0.8,
        pad_token_id=tokenizer.pad_token_id,
        bos_token_id=tokenizer.bos_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

result = tokenizer.decode(output_ids.tolist()[0])
print(result)
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