BiniGPT-0.1B-FM

Model Description

Welcome to BiniGPT-0.1B-FM! This is my very first model upload to Hugging Face.

I am uploading this to establish my deployment pipeline and lay the groundwork for my future custom model series. This repository hosts weight configurations originating from the open-source GPT-2 model series developed and released by OpenAI. All credit for the baseline architecture and primary pretraining goes to the original authors. The model is distributed under the permissive MIT License.

• Model name: BiniGPT-0.1B-FM
• Model type: Causal Language Model (Transformer Decoder)
• Base model: GPT 2
• Language(s) (NLP): English
• License: MIT
• Shared by: Abhishek Kumar (KumarXAI)

Direct Use

This model is best used to test inference performance, validate local pipeline architectures, or experiment with few-shot prompting templates to direct next-token behavior.

Quickstart: Run in 30 Seconds

Ensure you have transformers and torch installed, then run the snippet below:

pip install transformers torch

1. Using the Pipeline (High-Level Helper) You can test the model easily using Hugging Face's high-level pipeline helper. This automatically handles downloading the weights, setting up the tokenizer, and generating text:

from transformers import pipeline

# Use a pipeline as a high-level helper
pipe = pipeline("text-generation", model="KumarXAI/BiniGPT-0.1B-FM")

# Run inference on a prompt
prompt = "The secret of scientific discovery is"
outputs = pipe(prompt, max_new_tokens=25, do_sample=True, temperature=0.7)
print(outputs[0]["generated_text"])

1. Loading Model and Tokenizer Directly If you need to interact directly with the model's inner workings (the building blocks of AI) to customize generation parameters:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model directly
tokenizer = AutoTokenizer.from_pretrained("KumarXAI/BiniGPT-0.1B-FM")
model = AutoModelForCausalLM.from_pretrained("KumarXAI/BiniGPT-0.1B-FM")

# Setup input
prompt = "In the heart of Mithila, a great scholar discovered"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate with custom settings
output_ids = model.generate(
    **inputs,
    max_new_tokens=50,
    do_sample=True,
    temperature=0.8,
    top_p=0.95,
    pad_token_id=tokenizer.eos_token_id
)

print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

Out-of-Scope Use

• No Safety Alignment: Because this is a raw base model, it has not undergone RLHF (Reinforcement Learning from Human Feedback) or safety instruction tuning.
• Not a Conversational Model: It will naturally seek to complete text blocks rather than answer questions like an assistant.
• Hallucinations: The model is highly prone to factual errors, generating biased language, and repeating phrases. Do not rely on it for critical factual retrieval.

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

Use the code below to get started with the model.

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Training Details

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

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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 presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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