---
library_name: transformers
tags: []
---

# Model Card for Model ID

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## Model Details

### Model Description

Initially, Pixprompt is the first open-source small LLM, Pixprompt combines a CLIP vision encoder and GPT-2 (125M) decoder, with optional LoRA adapters for efficient fine-tuning. It was originally trained to support image + prompt → text, and now fine-tuned on a curated set of financial data and news headlines fetched dynamically from the web.


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:** [Sree bhargavi balija]
- **Funded by [optional]:** [self]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [Multimodal Causal Language Model (CLIP + GPT2)]
- **Language(s) (NLP):** [English]
- **License:** [MIT]
- **Finetuned from model [optional]:** [bhargavi909/Pixprompt]

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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

from transformers import GPT2LMHeadModel, GPT2Tokenizer
from peft import PeftModel

base = GPT2LMHeadModel.from_pretrained("bhargavi909/Pixprompt")
model = PeftModel.from_pretrained(base, "./finetuned-financial-pixprompt")
tokenizer = GPT2Tokenizer.from_pretrained("bhargavi909/Pixprompt")

prompt = "The chart shows the impact of inflation"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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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

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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### Results

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#### Summary



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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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#### Hardware

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#### Software

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## Citation [optional]

@misc{pixprompt2024,
  author = {Sree Bhargavi Balija},
  title = {Pixprompt: A Multimodal GPT Model for Financial Text Generation},
  year = {2024},
  url = {https://huggingface.co/bhargavi909/Pixprompt},
}

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## Glossary [optional]

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