---
license: mit
language:
- en
base_model:
- distilbert/distilgpt2
---

# Model Card for LockinGPT

<!-- Provide a quick summary of what the model is/does. -->

LockinGPT is a fine-tuned language model based on `distilgpt2`, optimized for generating conversational questions and creative prompts related to blockchain topics, especially focusing on Solana-based ecosystems.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

LockinGPT is specifically fine-tuned for generating yes/no questions and other conversational content related to the Solana blockchain and $LOCKIN token ecosystem. It is designed to aid developers, investors, and enthusiasts in generating useful blockchain-related queries. The model was fine-tuned using a curated dataset of Solana-related content to ensure relevance and accuracy.

- **Developed by:** Jonathan Gan
- **Funded by [optional]:** Self-funded
- **Shared by [optional]:** Jonathan Gan
- **Model type:** Causal Language Model
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** distilbert/distilgpt2

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** Private repository (contact Jonathan Gan for details)
- **Paper [optional]:** N/A
- **Demo [optional]:** N/A

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

- Generating blockchain-related questions for interactive use.
- Conversational tasks related to the Solana ecosystem.

### Downstream Use [optional]

- Fine-tuned for specific blockchain or crypto-related chatbot applications.

### Out-of-Scope Use

- Non-English conversational tasks.
- Topics unrelated to blockchain or cryptocurrency may produce incoherent outputs.
- Sensitive or adversarial applications.

## Bias, Risks, and Limitations

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

- The model is fine-tuned on Solana-related content and may not generalize well outside this domain.
- It may reflect biases present in the training data (e.g., promotion of specific blockchain technologies over others).

### Recommendations

Users should verify generated content for factual accuracy, especially in contexts requiring precision (e.g., financial advice or technical implementation).

## How to Get Started with the Model

Use the code below to get started with LockinGPT:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("./lockin_model")
model = AutoModelForCausalLM.from_pretrained("./lockin_model")

prompt = "Generate a yes/no question about the $LOCKIN token"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(inputs["input_ids"], max_new_tokens=50, do_sample=True, top_p=0.9, temperature=1.3)
print(tokenizer.decode(output[0], skip_special_tokens=True))