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--- |
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base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit |
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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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- trl |
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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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# Uploaded model |
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- **Developed by:** iFaz |
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- **License:** apache-2.0 |
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- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct-bnb-4bit |
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# Model Card: `unsloth/Llama-3.2-3B-Instruct-bnb-4bit` |
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## Overview |
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This is a fine-tuned version of the `unsloth/Llama-3.2-3B-Instruct-bnb-4bit` model, optimized for instruction-following tasks. The model leverages the efficiency of 4-bit quantization, making it lightweight and resource-efficient while maintaining high-quality outputs. It is particularly suited for text generation tasks in English, with applications ranging from conversational AI to natural language understanding tasks. |
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## Key Features |
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- **Base Model:** `unsloth/Llama-3.2-3B` |
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- **Quantization:** Utilizes 4-bit precision, enabling deployment on resource-constrained systems while maintaining performance. |
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- **Language:** English-focused, with robust generalization capabilities across diverse text-generation tasks. |
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- **Fine-Tuning:** Enhanced for instruction-following tasks to generate coherent and contextually relevant responses. |
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- **Versatile Applications:** Ideal for text generation, summarization, dialogue systems, and other natural language processing (NLP) tasks. |
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## Model Details |
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- **Developer:** iFaz |
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- **License:** Apache 2.0 (permitting commercial and research use) |
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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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- TRL (Transformers Reinforcement Learning) |
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## Usage |
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This model is designed for use in text-generation pipelines and can be easily integrated with the Hugging Face Transformers library. Its optimized architecture allows for inference on low-resource hardware, making it an excellent choice for applications that require efficient and scalable NLP solutions. |
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### Example Code: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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# Load the model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("iFaz/llama32_3B_en_emo_v1") |
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model = AutoModelForCausalLM.from_pretrained("iFaz/llama32_3B_en_emo_v1") |
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# Generate text |
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input_text = "Explain the benefits of AI in education." |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=100) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Performance |
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The fine-tuned model demonstrates strong performance on instruction-based tasks, providing detailed and contextually accurate responses. The 4-bit quantization enhances its speed and reduces memory consumption, enabling usage on devices with limited computational resources. |
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## Applications |
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- **Conversational AI:** Develop chatbots and virtual assistants with coherent, context-aware dialogue generation. |
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- **Text Summarization:** Extract concise summaries from lengthy texts for improved readability. |
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- **Creative Writing:** Assist in generating stories, articles, or creative content. |
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- **Education:** Enhance e-learning platforms with interactive and adaptive learning tools. |
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## Limitations and Considerations |
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- **Language Limitation:** Currently optimized for English. Performance on other languages may be suboptimal. |
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- **Domain-Specific Knowledge:** While the model performs well on general tasks, it may require additional fine-tuning for domain-specific applications. |
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## About the Developer |
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This model was developed and fine-tuned by **iFaz**, leveraging the capabilities of the `unsloth/Llama-3.2-3B` architecture to create an efficient and high-performance NLP tool. |
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## Acknowledgments |
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The model builds upon the `unsloth/Llama-3.2-3B` framework and incorporates advancements in quantization techniques. Special thanks to the Hugging Face community for providing tools and resources to support NLP development. |
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## License |
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The model is distributed under the Apache 2.0 License, allowing for both research and commercial use. For more details, refer to the [license documentation](https://opensource.org/licenses/Apache-2.0). |
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