Model Card for Model ID

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses


import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import re



model_id = "jaeyoungk/albatross" 
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B-Instruct')
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map='auto')



def gen(x):
    system_prompt = f"""
    Make a trading decision based on the following data.
    Please respond with a JSON object in the following format: 
    {{"investment_decision": string, "summary_reason": string, "short_memory_index": number, "middle_memory_index": number, "long_memory_index": number, "reflection_memory_index": number}}
    investment_decision must always be one of {{buy, sell, hold}}
    """

    # Tokenizing the input and generating the output
   
    inputs = tokenizer(
    [
        f"<|start_header_id|>system<|end_header_id|>{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>{x}<|end_header_id|>"
    ], return_tensors = "pt").to("cuda")

    
    gened = model.generate(
        **inputs,
        max_new_tokens=256,
        early_stopping=True,
        
    )
    
    full_text = tokenizer.decode(gened[0])
    
    # Finding the second occurrence of 'user<|end_header_id|'
    start_phrase = "user<|end_header_id|>"
    first_occurrence = full_text.find(start_phrase)
    second_occurrence = full_text.find(start_phrase, first_occurrence + len(start_phrase))
    
    if second_occurrence == -1:
        # If the second occurrence is not found, fallback to using the first occurrence
        start_idx = first_occurrence + len(start_phrase)
    else:
        start_idx = second_occurrence + len(start_phrase)
    
    # Find the index of the next special token after the start index
    end_idx = full_text.find('\\<|eot_id|', start_idx)

    # Extract the text between start_idx and end_idx
    extracted_text = full_text[start_idx:end_idx].strip()

    return extracted_text

# test the model
gen('input your text here')

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]