--- language: - en license: apache-2.0 tags: - lora-alpaca - alpaca - lora - LLaMA - Stanford Alpaca datasets: - mrzlab630/trading-candles pipeline_tag: question-answering widget: - text: "identify candle" context: "open: 38752.71, close: 38843.7, high: 38847.4, low: 38752.71" example_title: "identify candle" - text: "find candle" context: "38811.24,38838.41,38846.71,38736.24,234.00,45275276.00,59816.00,441285.00,645.00,84176.00,1694619.00,15732335.00" example_title: "find candle" - text: "find candle: Bullish" context: "38751.32,38818.6,38818.6,38695.03,62759348.00,2605789.00,71030.00,820738.00,59659.00,724738.00,7368363.00,50654.00" example_title: "find candle: Bullish" --- ## About: The model was fine-tuned on the LLaMA 7B. [weights_Llama_7b](https://huggingface.co/mrzlab630/weights_Llama_7b) the model is able to identify trading candles. the model knows about: - Four Price Doji, - Inverted Hammer, - Hammer, - Hanging Man, - Doji, - Long-legged doji, - Dragonfly doji, - Inverted Doji, - Bullish, - Bearish ## Prompts: ``` Instruction: identify candle Input: open:241.5,close:232.9, high:241.7, low:230.8 or Input: 241.5,232.9,241.7,230.8 Output: Bearish ``` ``` Instruction: identify candle Input: open:241.5,close:232.9, high:241.7, low:230.8 or Input: 241.5,232.9, 241.7,230.8 Output: Doji ``` ``` Instruction: identify candle:open:241.5,close:232.9, high:241.7, low:230.8 or Instruction: identify candle:241.5,232.9,241.7, 230.8 Output: Bearish:241.5,close:232.9, high:241.7, low:230.8 ``` ``` Instruction: find candle Input: 38811.24,38838.41,38846.71,38736.24,234.00,45275276.00,59816.00,441285.00,645.00,84176.00,1694619.00,15732335.00 Output: Dragonfly doji:38811.24,38838.41,38846.71,38736.24 ``` Instruction: find candle: {%candleName%} ``` Instruction: find candle: Bullish Input: 38751.32,38818.6,38818.6,38695.03,62759348.00,2605789.00,71030.00,820738.00,59659.00,724738.00,7368363.00,50654.00 Output: Bullish:38751.32,38818.6,38818.6,38695.03 ``` ### RUN ``` import sys import torch from peft import PeftModel import transformers import gradio as gr assert ( "LlamaTokenizer" in transformers._import_structure["models.llama"] ), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git" from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig SHARE_GRADIO=True LOAD_8BIT = False BASE_MODEL = "mrzlab630/weights_Llama_7b" LORA_WEIGHTS = "mrzlab630/lora-alpaca-trading-candles" tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL) if torch.cuda.is_available(): device = "cuda" else: device = "cpu" try: if torch.backends.mps.is_available(): device = "mps" except: pass if device == "cuda": model = LlamaForCausalLM.from_pretrained( BASE_MODEL, load_in_8bit=LOAD_8BIT, torch_dtype=torch.float16, device_map="auto", ) model = PeftModel.from_pretrained( model, LORA_WEIGHTS, torch_dtype=torch.float16, ) elif device == "mps": model = LlamaForCausalLM.from_pretrained( BASE_MODEL, device_map={"": device}, torch_dtype=torch.float16, ) model = PeftModel.from_pretrained( model, LORA_WEIGHTS, device_map={"": device}, torch_dtype=torch.float16, ) else: model = LlamaForCausalLM.from_pretrained( BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True ) model = PeftModel.from_pretrained( model, LORA_WEIGHTS, device_map={"": device}, ) 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} ### Response:""" else: return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response:""" if not LOAD_8BIT: model.half() # seems to fix bugs for some users. 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() gr.Interface( fn=evaluate, inputs=[ gr.components.Textbox( lines=2, label="Instruction", placeholder="Tell me about alpacas." ), gr.components.Textbox(lines=2, label="Input", placeholder="none"), gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"), gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"), gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"), gr.components.Slider(minimum=1, maximum=4, step=1, value=4, label="Beams"), gr.components.Slider( minimum=1, maximum=2000, step=1, value=128, label="Max tokens" ), ], outputs=[ gr.inputs.Textbox( lines=5, label="Output", ) ], title="💹 🕯 Alpaca-LoRA-Trading-Candles", description="Alpaca-LoRA-Trading-Candles is a 7B-parameter LLaMA model tuned to execute instructions. It is trained on the [trading candles] dataset(https://huggingface.co/datasets/mrzlab630/trading-candles) and uses the Huggingface LLaMA implementation. For more information, visit [project website](https://huggingface.co/mrzlab630/lora-alpaca-trading-candles).\nPrompts:\nInstruction: identify candle, Input: open:241.5,close:232.9, high:241.7, low:230.8\nInstruction: find candle, Input: 38811.24,38838.41,38846.71,38736.24,234.00,45275276.00,59816.00,441285.00,645.00,84176.00,1694619.00,15732335.00\nInstruction: find candle: Bullish, Input: 38751.32,38818.6,38818.6,38695.03,62759348.00,2605789.00,71030.00,820738.00,59659.00,724738.00,7368363.00,50654.00", ).launch(server_name="0.0.0.0", share=SHARE_GRADIO) ```