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Albatross

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metadata
library_name: transformers
tags: []

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.

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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')

``` python 

### Direct Use

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### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

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### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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## Citation [optional]

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## Glossary [optional]

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