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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 7B 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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Training data source: BTC/USD provided by [Binance](https://www.binance.com/). |
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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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tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True) |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.padding_side = "right" |
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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BTC虛擬貨幣分析師", |
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}, |
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{"role": "user", "content": "今天是2024-04-21,昨日開盤價為64437.18,最高價為64960.37,最低價為62953.90,收盤價為64808.35,交易量為808273.27。請預測今日BTC的收盤價?"}, |
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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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### Framework versions |
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- PEFT 0.10.0 |