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--- |
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base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-generation |
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- instruction-following |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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--- |
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# SmolLM2-1.7B-Instruct |
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**Developed by:** Daemontatox |
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**Model Type:** Fine-tuned Language Model (LLM) |
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**Base Model:** [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co./HuggingFaceTB/SmolLM2-1.7B-Instruct) |
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**Finetuned from model:** HuggingFaceTB/SmolLM2-1.7B-Instruct |
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**License:** apache-2.0 |
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**Languages:** en |
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**Tags:** |
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- text-generation |
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- instruction-following |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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## Model Description |
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SmolLM2-1.7B-Instruct is a fine-tuned version of [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co./HuggingFaceTB/SmolLM2-1.7B-Instruct), optimized for general-purpose instruction-following tasks. This model combines the efficiency of the LLaMA architecture with fine-tuning techniques to enhance performance in: |
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- Instruction adherence and task-specific prompts. |
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- Creative and coherent text generation. |
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- General-purpose reasoning and conversational AI. |
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The fine-tuning process utilized [Unsloth](https://github.com/unslothai/unsloth) and the Hugging Face TRL library, achieving a 2x faster training time compared to traditional methods. This efficiency allows for resource-conscious model updates while retaining high-quality performance. |
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## Intended Uses |
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SmolLM2-1.7B-Instruct is designed for: |
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- Generating high-quality text for a variety of applications, such as content creation and storytelling. |
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- Following complex instructions across different domains. |
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- Supporting research and educational use cases. |
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- Serving as a lightweight option for conversational agents. |
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## Limitations |
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While the model excels in instruction-following tasks, it has certain limitations: |
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- May exhibit biases inherent in the training data. |
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- Limited robustness for highly technical or specialized domains. |
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- Performance may degrade with overly complex or ambiguous prompts. |
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## How to Use |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "daemontatox/smollm2-1.7b-instruct" # Replace with the actual model name |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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# Example usage |
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prompt = "Explain the importance of biodiversity in simple terms: " |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs) |
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(generated_text) |
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``` |
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## Acknowledgements |
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Special thanks to the Unsloth team for their tools enabling efficient fine-tuning. The model was developed with the help of open-source libraries and community resources. |
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