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--- |
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base_model: unsloth/Meta-Llama-3.1-8B-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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- sft |
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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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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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# Uploaded model: Llama 3.1 8B Finetuned |
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- **Developed by:** kparkhade |
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- **License:** apache-2.0 |
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- **Base model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit |
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## Overview |
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This fine-tuned Llama 3.1 8B model was optimized for efficient text generation tasks. |
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By leveraging advanced optimization techniques from [Unsloth](https://github.com/unslothai/unsloth) and [Hugging Face's](https://huggingface.co/docs/trl/) TRL library, |
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training was completed 2x faster than conventional methods. |
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### Key Features |
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- **Speed Optimized:** Training was accelerated with the Unsloth framework, significantly reducing resource consumption. |
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- **Model Compatibility:** Compatible with Hugging Face's ecosystem for seamless integration. |
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- **Quantization:** Built on a 4-bit quantized base model for efficient deployment and inference. |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load the model and tokenizer |
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model_name = "kparkhade/Llama-3.1-8B" |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Generate text |
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inputs = tokenizer("Your input prompt here", return_tensors="pt") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Applications |
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This model can be used for: |
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- Creative writing (e.g., story or poetry generation) |
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- Generating conversational responses |
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- Assisting with coding-related queries |
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## Acknowledgements |
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Special thanks to the [Unsloth](https://github.com/unslothai/unsloth) team for providing tools that make model fine-tuning faster and more efficient. |
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