---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
---
 
![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/EaSsXHEv3KS2hMQWJ7mEf.png)

# **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**

1. **Compact & Efficient Architecture**
   Built on the proven **LlamaForCausalLM architecture** with only 500M parameters, ensuring fast inference and low memory footprint for resource-constrained environments.

2. **General-Purpose Conversational AI**
   Optimized for natural dialogue, casual conversations, and simple Q&A tasks—perfect for chatbots, virtual assistants, and interactive applications.

3. **Llama2-Based Training**
   Leverages the robust foundation of the **Llama2 Group of models**, inheriting their conversational capabilities while maintaining ultra-lightweight deployment requirements.

4. **Fast Response Generation**
   Designed for quick inference with minimal latency, making it suitable for real-time chat applications and interactive user experiences.

5. **Versatile Deployment Options**
   Runs efficiently on **CPUs**, **entry-level GPUs**, **mobile devices**, and **edge computing platforms** with minimal resource requirements.

6. **Simple Integration**
   Easy to integrate into existing applications with standard transformer interfaces and minimal setup requirements.

---

## **Quickstart with Transformers**

```python
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.