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--- |
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license: mit |
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library_name: transformers |
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--- |
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# Gtp4all-lora |
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Model Description |
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The gtp4all-lora model is a custom transformer model designed for text generation tasks. |
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It is taken from nomic-ai's GPT4All code, which I have transformed to the current format. |
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This model is trained on a diverse dataset and fine-tuned to generate coherent and contextually relevant text. |
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The model is inspired by GPT-4 and tailored to include the LoRa (Long Range) aspect, which can be useful for generating content related to long-range communication technology. |
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## Training Data |
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The model is trained on a custom dataset that includes a variety of sources such as: |
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Books, articles, and blogs related to LoRa technology |
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General technology news and discussions |
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Webpages and forum threads about IoT, LPWAN, and other related topics |
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The dataset has been preprocessed and cleaned to remove any irrelevant or inappropriate content. The training data is balanced to ensure a comprehensive understanding of the topics related to LoRa and IoT. |
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## Usage |
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You can use this model with the Hugging Face Transformers library. Here's an example of how to generate text using the gtp4all-lora model: |
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```python |
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from transformers import pipeline |
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model_name = "matthieunlp/gtp4all-lora" |
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generator = pipeline("text-generation", model=model_name, tokenizer=model_name) |
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prompt = "LoRa is a technology that can be used for" |
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generated_text = generator(prompt, max_length=100, num_return_sequences=1) |
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print(generated_text[0]['generated_text']) |
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``` |
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## Limitations |
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This model has some limitations: |
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The model may not perform equally well on all sub-domains of IoT and long-range communication technology. |
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It may generate text that is biased or incorrect due to the nature of the training data. |
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The model may not be suitable for tasks other than text generation. |
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Please provide feedback or report any issues to help improve the model's performance and reliability. |
