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