DavidLanz
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llama2_7b_taiwan_btc_qlora

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 ---
 library_name: peft
 base_model: DavidLanz/Llama2-tw-7B-v2.0.1-chat
 ---
 # Model Card for Model ID
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 ## Model Details
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 ### Framework versions
 - PEFT 0.10.0

 ---
 library_name: peft
 base_model: DavidLanz/Llama2-tw-7B-v2.0.1-chat
+inference: false
+language:
+- en
+license: llama2
+model_creator: Meta Llama 2
+model_name: Llama 2 7B Chat
+model_type: llama
+pipeline_tag: text-generation
+quantized_by: QLoRA
+tags:
+- facebook
+- meta
+- pytorch
+- llama
+- llama-2
 ---
 # Model Card for Model ID
+This PEFT weight is for predicting BTC price.
+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
+Training data source: BTC/USD provided by [Binance](https://www.binance.com/).
+### Model Description
+This repo contains QLoRA format model files for [Meta's Llama 2 7B-chat](https://huggingface.co/DavidLanz/Llama2-tw-7B-v2.0.1-chat).
 ## 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/Llama2-tw-7B-v2.0.1-chat"
+adapter_path = "DavidLanz/llama2_7b_taiwan_btc_qlora"
+base_model = AutoModelForCausalLM.from_pretrained(
+    based_model_path,
+    low_cpu_mem_usage=True,
+    # load_in_4bit=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(base_model_path, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+from transformers import pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, device_map="auto")
+messages = [
+    {
+        "role": "system",
+        "content": "你是一位專業的股票分析師",
+    },
+    {"role": "user", "content": "昨日開盤價為42950.02，最高價為43581.3，最低價為40610.0，收盤價為41319.11，交易量為3175.25156。請預測今日股票的收盤價?"},
+]
+prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```
 ### Framework versions
 - PEFT 0.10.0