| --- |
| 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. |
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| 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. |
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| - **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) |
|
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| ### 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. |
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| **Quickstart: Run in 30 Seconds** |
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| 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. |
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| ## Bias, Risks, and Limitations |
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| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| [More Information Needed] |
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| ### Recommendations |
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| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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. |
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| ## How to Get Started with the Model |
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| Use the code below to get started with the model. |
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| [More Information Needed] |
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| ## Training Details |
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| ### Training Data |
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| <!-- 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. --> |
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| [More Information Needed] |
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| ### Training Procedure |
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| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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| #### Preprocessing [optional] |
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| [More Information Needed] |
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| #### Training Hyperparameters |
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| - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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| #### Speeds, Sizes, Times [optional] |
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| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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| [More Information Needed] |
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| ## Evaluation |
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| <!-- This section describes the evaluation protocols and provides the results. --> |
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| ### Testing Data, Factors & Metrics |
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| #### Testing Data |
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| <!-- This should link to a Dataset Card if possible. --> |
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| [More Information Needed] |
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| #### Factors |
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| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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| [More Information Needed] |
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| #### Metrics |
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| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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| [More Information Needed] |
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| ### Results |
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| [More Information Needed] |
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| #### Summary |
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| ## Model Examination [optional] |
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| <!-- Relevant interpretability work for the model goes here --> |
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| [More Information Needed] |
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| ## Environmental Impact |
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| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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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| - **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] |
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| ## Technical Specifications [optional] |
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| ### Model Architecture and Objective |
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| [More Information Needed] |
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| ### Compute Infrastructure |
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| [More Information Needed] |
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| #### Hardware |
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| [More Information Needed] |
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| #### Software |
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| [More Information Needed] |
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| ## Citation [optional] |
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| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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| **BibTeX:** |
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| [More Information Needed] |
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| **APA:** |
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| [More Information Needed] |
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| ## Glossary [optional] |
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| <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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| [More Information Needed] |
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| ## More Information [optional] |
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| [More Information Needed] |
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| ## Model Card Authors [optional] |
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| [More Information Needed] |
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| ## Model Card Contact |
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| [More Information Needed] |
