T-CLM2

Model Card for T-CLM2

This model card provides essential information about the T-CLM2 model, designed for text generation tasks.

Model Details

Model Description

T-CLM2 is a transformer-based model specifically designed for text generation. It can generate coherent and contextually relevant text based on input prompts, making it suitable for various applications such as creative writing, content generation, and more.

• Developed by: Mayura Patkar
• Model type: Transformer Decoder only model
• Language(s): English

Uses

Direct Use

The model is intended for generating text based on user-defined prompts. Users can input any text, and the model will generate a continuation that is coherent and contextually relevant.

Downstream Use [optional]

T-CLM2 can be integrated into applications that require text generation capabilities, such as chatbots, content creation tools, or automated storytelling applications.

Out-of-Scope Use

The model should not be used for generating harmful or malicious content, including but not limited to hate speech, misinformation, or illegal content. It may also not perform well for tasks that require specific domain knowledge or factual accuracy.

Bias, Risks, and Limitations

While T-CLM2 has been trained to generate diverse text, it may reflect biases present in the training data. Users should be cautious and review generated outputs for appropriateness and accuracy, particularly in sensitive contexts.

Recommendations

Users should understand the potential biases in the generated text and verify outputs for accuracy, especially in applications with significant impacts (e.g., legal or medical advice).

How to Get Started with the Model

To get started, you can use the following code snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Mayura01/T-CLM2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Once upon a time in a faraway land,"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(input_ids, max_length=100)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(generated_text)