---
library_name: transformers
license: mit
language:
- en
base_model:
- openai-community/gpt2
tags:
- BiniGPT
---

### 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:
```bash
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:
```python
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"])
```

2. 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:

```python
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

### Training Data

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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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