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RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf

sourceHugging Faceupdated 2y agoView on Hugging Face
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Quantization made by Richard Erkhov.

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gemma-2b-ForexAI - GGUF

  • —Model creator: https://huggingface.co/mjmanashti/
  • —Original model: https://huggingface.co/mjmanashti/gemma-2b-ForexAI/

Original model description: --- license: other tags:

  • —autotrain
  • —text-generation widget:
  • —text: 'I love AutoTrain because ' ---

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Usage

python
!pip install transformers

!pip install accelerate


from huggingface_hub import notebook_login
notebook_login()

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch


tokenizer = AutoTokenizer.from_pretrained("mjmanashti/gemma-2b-ForexAI")
torch.set_default_dtype(torch.float16)

model = AutoModelForCausalLM.from_pretrained("mjmanashti/gemma-2b-ForexAI", device_map="auto")

chat = [
    { "role": "user", "content": "Based on the following input data: [Time: 2024-01-29 23:00:00, Open: 1.0834, High: 1.0837, Low: 1.08334, Close: 1.08338, Volume: 722] what trading signal (BUY, SELL, or HOLD) should be executed to maximize profit? If the signal is BUY, what would be the entry price and If the signal is SELL, what would be the exit price for profit maximization? " },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
print(tokenizer.decode(outputs[0]))