LLaMa3-8b-WangchanX-sft-Demo

Built with Meta Llama 3 (Fine tuning with Qlora)

This model is based on WangchanX Fine-tuning Pipeline.

GitHub: WangchanX Fine-tuning Pipeline.

License: Meta Llama 3 Community License

Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.

Train Example

Train WangchanX pipeline: Colab

Inference Example

Run on Colab

Prepare your model and tokenizer:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Model path
path = "airesearch/LLaMa3-8b-WangchanX-sft-Demo"

# Device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(path, device_map="auto")

Define chat messages:

messages = [
    {"role": "user", "content": "ลิเก กับ งิ้ว ต่างกันอย่างไร"},
]

Tokenize chat messages:

tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(device)
print(tokenizer.decode(tokenized_chat[0]))

Generate responses:

outputs = model.generate(tokenized_chat, max_length=2048)
print(tokenizer.decode(outputs[0]))