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--- |
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base_model: meta-llama/Llama-3.2-3B-Instruct |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- gguf |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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<div align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/669777597cb32718c20d97e9/4emWK_PB-RrifIbrCUjE8.png" |
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alt="Title card" |
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style="width: 500px; |
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height: auto; |
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object-position: center top;"> |
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</div> |
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# Uploaded Model |
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- **Developed by:** Alpha AI |
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- **License:** apache-2.0 |
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- **Finetuned from model:** meta-llama/Llama-3.2-3B-Instruct |
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library. |
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## AlphaAI-Chatty-INT2 |
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### Overview |
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AlphaAI-Chatty-INT2 is a fine-tuned meta-llama/Llama-3.2-3B-Instruct model optimized for **empathic**, **chatty**, and **engaging** conversations. Building on the foundations of our INT1 release, the INT2 version includes enhanced conversational capabilities that make it more context-aware, responsive, and personable. Trained on an improved proprietary conversational dataset, this model is particularly suitable for local deployments requiring a **natural, interactive, and empathetic** dialogue experience. |
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The model is available in GGUF format and has been quantized to different levels to support various hardware configurations. |
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This model is an upgrade to AlphaAI-Chatty-INT1. You can find and use the previous models from [here.](https://huggingface.co/alpha-ai/AlphaAI-Chatty-INT1) |
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### Model Details |
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- **Base Model:** meta-llama/Llama-3.2-3B-Instruct |
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- **Fine-tuned By:** Alpha AI |
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- **Training Framework:** Unsloth |
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#### Quantization Levels Available |
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- q4_k_m |
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- q5_k_m |
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- q8_0 |
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- 16-bit (this, full precision) - [Link](https://huggingface.co/alphaaico/AlphaAI-Chatty-INT2) |
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*(Note: The INT1 16-bit link is referenced (https://huggingface.co/alphaaico/AlphaAI-Chatty-INT1)* |
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**Format:** GGUF (Optimized for local deployments, https://huggingface.co/alphaaico/AlphaAI-Chatty-INT2-GGUF) |
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### Use Cases |
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- **Conversational AI** – Ideal for chatbots, virtual assistants, and customer support where empathetic and engaging interaction is crucial. |
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- **Local AI Deployments** – Runs efficiently on local machines, negating the need for cloud-based inference. |
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- **Research & Experimentation** – Suitable for studying advanced conversational AI techniques and fine-tuning on specialized or proprietary datasets. |
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### Model Performance |
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AlphaAI-Chatty-INT2 has been further optimized to deliver: |
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- **Empathic and Context-Aware Responses** – Improved understanding of user inputs with a focus on empathetic replies. |
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- **High Efficiency on Consumer Hardware** – Maintains quick inference speeds even with more advanced conversation modeling. |
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- **Balanced Coherence and Creativity** – Strikes an ideal balance for real-world dialogue applications, allowing for both coherent answers and creative flair. |
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### Limitations & Biases |
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Like any AI system, this model may exhibit biases stemming from its training data. Users should employ it responsibly and consider additional fine-tuning if needed for sensitive or specialized applications. |
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### License |
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Released under the **Apache-2.0** license. For full details, please consult the license file in the Hugging Face repository. |
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### Acknowledgments |
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Special thanks to the Unsloth team for their optimized training pipeline for LLaMA models. Additional appreciation goes to Hugging Face’s TRL library for enabling accelerated and efficient fine-tuning workflows. |
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