Kapteyn-500M

Kapteyn-500M is a lightweight, general-purpose micro language model based on the LlamaForCausalLM architecture and trained on the Llama2 Group of models. This compact 500M parameter model is designed for simple chats and responses, making it ideal for conversational AI applications where efficiency and quick response times are prioritized over complex reasoning tasks.

Key Features

Compact & Efficient Architecture Built on the proven LlamaForCausalLM architecture with only 500M parameters, ensuring fast inference and low memory footprint for resource-constrained environments.
General-Purpose Conversational AI Optimized for natural dialogue, casual conversations, and simple Q&A tasks—perfect for chatbots, virtual assistants, and interactive applications.
Llama2-Based Training Leverages the robust foundation of the Llama2 Group of models, inheriting their conversational capabilities while maintaining ultra-lightweight deployment requirements.
Fast Response Generation Designed for quick inference with minimal latency, making it suitable for real-time chat applications and interactive user experiences.
Versatile Deployment Options Runs efficiently on CPUs, entry-level GPUs, mobile devices, and edge computing platforms with minimal resource requirements.
Simple Integration Easy to integrate into existing applications with standard transformer interfaces and minimal setup requirements.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Kapteyn-500M"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Hello! How are you doing today?"

messages = [
    {"role": "system", "content": "You are a helpful and friendly assistant."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.9
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Intended Use

Casual conversation and general chat applications
Simple Q&A systems and customer service bots
Educational tools requiring basic conversational interaction
Mobile and edge AI applications with limited computational resources
Prototyping conversational AI features before scaling to larger models
Personal assistants for everyday tasks and simple information retrieval

Limitations

Limited complex reasoning and analytical capabilities compared to larger models
Not suitable for specialized technical, scientific, or mathematical tasks
Context window limitations may affect longer conversations
May struggle with nuanced or highly specialized domain knowledge
Optimized for simple responses rather than detailed explanations or complex problem-solving.