bhargavi909/Pixprompt

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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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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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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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