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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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--- |
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# **Kapteyn-500M** |
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> **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. |
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--- |
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## **Key Features** |
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1. **Compact & Efficient Architecture** |
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Built on the proven **LlamaForCausalLM architecture** with only 500M parameters, ensuring fast inference and low memory footprint for resource-constrained environments. |
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2. **General-Purpose Conversational AI** |
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Optimized for natural dialogue, casual conversations, and simple Q&A tasks—perfect for chatbots, virtual assistants, and interactive applications. |
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3. **Llama2-Based Training** |
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Leverages the robust foundation of the **Llama2 Group of models**, inheriting their conversational capabilities while maintaining ultra-lightweight deployment requirements. |
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4. **Fast Response Generation** |
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Designed for quick inference with minimal latency, making it suitable for real-time chat applications and interactive user experiences. |
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5. **Versatile Deployment Options** |
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Runs efficiently on **CPUs**, **entry-level GPUs**, **mobile devices**, and **edge computing platforms** with minimal resource requirements. |
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6. **Simple Integration** |
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Easy to integrate into existing applications with standard transformer interfaces and minimal setup requirements. |
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--- |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Kapteyn-500M" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Hello! How are you doing today?" |
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messages = [ |
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{"role": "system", "content": "You are a helpful and friendly assistant."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=256, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.9 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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--- |
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## **Intended Use** |
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* Casual conversation and general chat applications |
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* Simple Q&A systems and customer service bots |
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* Educational tools requiring basic conversational interaction |
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* Mobile and edge AI applications with limited computational resources |
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* Prototyping conversational AI features before scaling to larger models |
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* Personal assistants for everyday tasks and simple information retrieval |
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--- |
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## **Limitations** |
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* Limited complex reasoning and analytical capabilities compared to larger models |
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* Not suitable for specialized technical, scientific, or mathematical tasks |
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* Context window limitations may affect longer conversations |
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* May struggle with nuanced or highly specialized domain knowledge |
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* Optimized for simple responses rather than detailed explanations or complex problem-solving. |
