BiniGPT-0.1B-FM/README.md

---
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

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]