---
library_name: transformers
datasets:
- Suraponn/thai_instruction_sft
language:
- th
base_model: meta-llama/Meta-Llama-3.1-8B
---


import json
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig

model_id = "Suraponn/llama_3.1_8B_Thai_instruct"

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    )

model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    device_map="cuda:0",
    torch_dtype=torch.float16,
    )

config_setting = AutoConfig.from_pretrained(
    model_id,
    add_special_tokens=True,
    )


if tokenizer.chat_template is None:
    tokenizer.chat_template = tokenizer.default_chat_template

if not "system" in tokenizer.chat_template and "system" in tokenizer.default_chat_template:
    tokenizer.chat_template = tokenizer.default_chat_template



s_split = "เขียนบทความเกี่ยวกับการออกกำลังกายให้ถูกต้อง"

chat = [
    {
        "role": "system", 
        "content":  "You are a helpfull assistant. Please respond in Thai."
    },
    {
        "role": "user", 
        "content": s_split,
    },
]

tokenizer.use_default_system_prompt = False
extract_input = tokenizer.apply_chat_template(chat, tokenize=False , add_generation_prompt=True)

#extract_input = extract_input.split(s_split)[0]
print("------------\n" + extract_input + "\n------------")


inputs = tokenizer(
    extract_input, 
    return_tensors="pt",
    add_special_tokens = False,
    )
#print(inputs)


terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>"),
]
#print(terminators)


inputs = inputs.to(model.device)

with torch.no_grad():
    tokens = model.generate(
        **inputs,
        max_new_tokens=2048,
        do_sample=True,
        eos_token_id=terminators,
        temperature=0.7,
        #top_p=1,
    )
    
    
output = tokenizer.decode(tokens[0])
print(output)