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---
base_model: DavidLanz/Llama3-tw-8B-Instruct
language:
- en
library_name: peft
license: apache-2.0
model_name: Llama 3 8B Instruct
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
- llama-3
inference: false
model_creator: Meta Llama 3
model_type: llama
quantized_by: QLoRA
---
# Model Card for Model ID
This PEFT model is designed for predicting the prices of these five Taiwan stocks:
| 證券代號 | 證券名稱 |
|---------|--------|
| 3661 | 世芯-KY |
| 2330 | 台積電 |
| 3017 | 奇鋐 |
| 2618 | 長榮航 |
| 2317 | 鴻海 |
Disclaimer: This model is for a time series problem on LLM performance, and it's not for investment advice; any prediction results are not a basis for investment reference.
## Model Details
The training data source is from the [臺灣證券交易所 / Taiwan Stock Exchange (TWSE)](https://www.twse.com.tw/), covering the period from January 1, 2019, to July 31, 2024 (5 years).
### Model Description
This repo contains QLoRA format model files for [Meta's Llama 3 8B Instruct](https://huggingface.co/DavidLanz/Llama3-tw-8B-Instruct).
## Uses
```python
import torch
from peft import LoraConfig, PeftModel
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
TextStreamer,
pipeline,
logging,
)
device_map = {"": 0}
use_4bit = True
bnb_4bit_compute_dtype = "float16"
bnb_4bit_quant_type = "nf4"
use_nested_quant = False
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
bnb_config = BitsAndBytesConfig(
load_in_4bit=use_4bit,
bnb_4bit_quant_type=bnb_4bit_quant_type,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=use_nested_quant,
)
based_model_path = "DavidLanz/Llama3-tw-8B-Instruct"
adapter_path = "DavidLanz/llama3_8b_taiwan_stock_qlora"
base_model = AutoModelForCausalLM.from_pretrained(
based_model_path,
low_cpu_mem_usage=True,
return_dict=True,
quantization_config=bnb_config,
torch_dtype=torch.float16,
device_map=device_map,
)
model = PeftModel.from_pretrained(base_model, adapter_path)
tokenizer = AutoTokenizer.from_pretrained(based_model_path, trust_remote_code=True)
import torch
from transformers import pipeline, TextStreamer
text_gen_pipeline = pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.bfloat16},
tokenizer=tokenizer,
)
messages = [
{
"role": "system",
"content": "你是一位專業的台灣股市交易分析師",
},
{"role": "user", "content": "鴻海上週五的表現，開盤價是211.00，當日最高價是217.50，當日最低價是211.00，收盤價是212.00，與前一天相比下跌了5.50，成交股數為174,076,905，交易金額為37,127,696,834。請預測今天的收盤價?"},
]
prompt = text_gen_pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
text_gen_pipeline.tokenizer.eos_token_id,
text_gen_pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = text_gen_pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
### Framework versions
- PEFT 0.11.1 |