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---
license: mit
language:
- th
pipeline_tag: text-generation
tags:
- instruction-finetuning
library_name: adapter-transformers
datasets:
- iapp_wiki_qa_squad
- tatsu-lab/alpaca
- wongnai_reviews
- wisesight_sentiment
---
# 🐃🇹🇭 Buffala-LoRa-TH
Buffala-LoRA is a 7B-parameter LLaMA model finetuned to follow instructions. It is trained on the Stanford Alpaca (TH Translated), Wisesignt, WikiTH, Pantip and IAppQ&A dataset and makes use of the Huggingface LLaMA implementation. For more information, please visit [the project's website](https://github.com/tloen/alpaca-lora).
## Issues and what next?
- The model still lacks a significant amount of world knowledge, so it is necessary to fine-tune it on larger Thai datasets > Next version: CCNet,OSCAR,thWiki
- Currently, there is no translation prompt. We plan to fine-tune the model on the SCB Thai-English dataset soon.
- The model works well with the LangChain Search agent (Serpapi), which serves as a hotfix for world knowledge. > Plan for Spaces with search chain demo
- Lacked of chat capabilities, waiting for LangChain implementation.
- Colab demo.
- Github for datasets and training notebook.
## How to use
```python
import torch
from peft import PeftModel
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
device = "cuda"
tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
model = LlamaForCausalLM.from_pretrained(
"decapoda-research/llama-7b-hf",
load_in_8bit=True,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(
model,
"Thaweewat/thai-buffala-lora-7b-v0-1",
torch_dtype=torch.float16,
)
def generate_prompt(instruction, input=None):
if input:
return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{input + get_list_and_snippet(instruction)}
### Response:"""
else:
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{get_list_and_snippet(instruction)}
### Response:"""
if not LOAD_8BIT:
model.half()
model.eval()
if torch.__version__ >= "2" and sys.platform != "win32":
model = torch.compile(model)
def evaluate(
instruction,
input=None,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
max_new_tokens=128,
**kwargs,
):
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
return output.split("### Response:")[1].strip()
evaluate(instruction = "จงแก้สมการต่อไปนี้ X เท่ากับเท่าไหร่", input="X+Y=15 and Y=7")
""" X = 8 """
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