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README.md
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library_name: peft
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base_model: DavidLanz/Llama2-tw-7B-v2.0.1-chat
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---
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# Model Card for Model ID
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## Model Details
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### Model Description
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- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Data Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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## Training procedure
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---
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library_name: peft
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base_model: DavidLanz/Llama2-tw-7B-v2.0.1-chat
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inference: false
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language:
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- en
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license: llama2
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model_creator: Meta Llama 2
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model_name: Llama 2 13B Chat
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model_type: llama
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pipeline_tag: text-generation
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quantized_by: QLoRA
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tags:
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- facebook
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- meta
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- pytorch
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- llama
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- llama-2
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---
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# Model Card for Model ID
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This PEFT weight is for predicting BTC price.
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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.
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## Model Details
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### Model Description
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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).
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## Uses
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```python
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import torch
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from peft import LoraConfig, PeftModel
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from transformers import (
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AutoModelForCausalLM,
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AutoTokenizer,
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BitsAndBytesConfig,
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HfArgumentParser,
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TrainingArguments,
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TextStreamer,
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pipeline,
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logging,
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)
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device_map = {"": 0}
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use_4bit = True
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bnb_4bit_compute_dtype = "float16"
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bnb_4bit_quant_type = "nf4"
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use_nested_quant = False
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compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=use_4bit,
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bnb_4bit_quant_type=bnb_4bit_quant_type,
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bnb_4bit_compute_dtype=compute_dtype,
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bnb_4bit_use_double_quant=use_nested_quant,
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)
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based_model_path = "DavidLanz/Llama2-tw-7B-v2.0.1-chat"
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adapter_path = "DavidLanz/llama2_7b_taiwan_btc_qlora"
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base_model = AutoModelForCausalLM.from_pretrained(
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based_model_path,
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low_cpu_mem_usage=True,
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load_in_4bit=True,
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return_dict=True,
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quantization_config=bnb_config,
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torch_dtype=torch.float16,
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device_map=device_map,
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)
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model = PeftModel.from_pretrained(base_model, adapter_path)
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import torch
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from transformers import pipeline
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, device_map="auto")
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messages = [
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{
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"role": "system",
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"content": "你是一位專業的股票分析師",
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},
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{"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。請預測今日股票的開盤價?"},
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]
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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## Training procedure
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