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--- |
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license: agpl-3.0 |
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language: |
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- en |
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base_model: |
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- meta-llama/Llama-3.2-1B-Instruct |
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library_name: predacons |
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tags: |
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- 'reasoning ' |
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- chain of thought |
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- problem solving |
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--- |
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## Model Details |
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### Model Description |
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Precacons/Pico-Lamma-3.2-1B-Reasoning-Instruct |
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Model Overview: Precacons/Pico-Lamma-3.2-1B-Reasoning-Instruct is a highly efficient and accurate language model fine-tuned on the “meta-llama/Llama-3.2-1B-Instruct” base model. Despite its compact size of just 0.99GB, it delivers exceptional performance, particularly in tasks requiring logical reasoning and structured thought processes. |
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- **Developed by:** [Shourya Shashank](https://huggingface.co/shouryashashank) |
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- **Model type:** Transformer-based Language Model |
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- **Language(s) (NLP):** English |
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- **License:** AGPL-3.0 |
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- **Finetuned from model [optional]:** meta-llama/Llama-3.2-1B-Instruct |
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#### Key Features: |
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* **Compact Size**: At only 0.99GB, it is lightweight and easy to deploy, making it suitable for environments with limited computational resources. |
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* **High Accuracy**: The model’s training on a specialized chain of thought and reasoning dataset enhances its ability to perform complex reasoning tasks with high precision. |
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* **Fine-Tuned on Meta-Llama**: Leveraging the robust foundation of the “meta-llama/Llama-3.2-1B-Instruct” model, it inherits strong language understanding and generation capabilities. |
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#### Applications: |
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* **Educational Tools**: Ideal for developing intelligent tutoring systems that require nuanced understanding and explanation of concepts. |
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* **Customer Support**: Enhances automated customer service systems by providing accurate and contextually relevant responses. |
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* **Research Assistance**: Assists researchers in generating hypotheses, summarizing findings, and exploring complex datasets. |
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## Uses |
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* Lightweight: The software is designed to be extremely lightweight, ensuring it can run efficiently on any system without requiring extensive resources. |
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* Natural Language Understanding: Ideal for applications requiring human-like text understanding and generation, such as chatbots, virtual assistants, and content generation tools. |
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* Small Size: Despite its compact size of just 0.99GB, it packs a powerful punch, making it easy to download and install. |
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* High Reliability: The reliability is significantly enhanced due to the chain-of-thought approach integrated into its design, ensuring consistent and accurate performance. |
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### Direct Use |
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* Problem Explanation: Generate detailed descriptions and reasoning for various problems, useful in educational contexts, customer support, and automated troubleshooting. |
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* Natural Language Understanding: Ideal for applications requiring human-like text understanding and generation, such as chatbots, virtual assistants, and content generation tools. |
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* Compact Deployment: Suitable for environments with limited computational resources due to its small size and 4-bit quantization. |
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### Downstream Use [optional] |
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* Educational Tools: Fine-tune the model on educational datasets to provide detailed explanations and reasoning for academic subjects. |
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* Customer Support: Fine-tune on customer service interactions to enhance automated support systems with accurate and context-aware responses. |
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## Bias, Risks, and Limitations |
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### Limitations |
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**Pico-Lamma-3.2-1B-Reasoning-Instruct** is a compact model designed for efficiency, but it comes with certain limitations: |
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3. **Limited Context Understanding**: |
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- With a smaller parameter size, the model may have limitations in understanding and generating contextually rich and nuanced responses compared to larger models. |
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4. **Bias and Fairness**: |
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- Like all language models, Pico-Lamma-3.2-1B-Reasoning-Instruct may exhibit biases present in the training data. Users should be cautious of potential biases in the generated outputs. |
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5. **Resource Constraints**: |
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- While the model is designed to be efficient, it still requires a GPU for optimal performance. Users with limited computational resources may experience slower inference times. |
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### Example Usage: |
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```python |
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import predacons |
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# Load the model and tokenizer |
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model_path = "Precacons/Pico-Lamma-3.2-1B-Reasoning-Instruct" |
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model = predacons.load_model(model_path) |
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tokenizer = predacons.load_tokenizer(model_path) |
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# Example usage |
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chat = [ |
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{"role": "user", "content": "A train travelling at a speed of 60 km/hr is stopped in 15 seconds by applying the brakes. Determine its retardation."}, |
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] |
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res = predacons.chat_generate(model = model, |
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sequence = chat, |
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max_length = 5000, |
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tokenizer = tokenizer, |
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trust_remote_code = True, |
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do_sample=True, |
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) |
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print(res) |
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``` |
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This example demonstrates how to load the `Pico-Lamma-3.2-1B-Reasoning-Instruct` model and use it to generate an explanation for a given query, keeping in mind the limitations mentioned above. |
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## Model Card Authors [optional] |
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[Shourya Shashank](https://huggingface.co/shouryashashank) |
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