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---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
---
# **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. |