prithivMLmods/Novaeus-Promptist-7B-Instruct

Novaeus-Promptist-7B-Instruct Uploaded Model Files

The Novaeus-Promptist-7B-Instruct is a fine-tuned large language model derived from the Qwen2.5-7B-Instruct base model. It is optimized for prompt enhancement, text generation, and instruction-following tasks, providing high-quality outputs tailored to various applications.

File Name [ Uploaded Files ]	Size	Description	Upload Status
.gitattributes	1.57 kB	Git attributes configuration for LFS.	Uploaded
README.md	400 Bytes	Documentation about the model.	Updated
added_tokens.json	657 Bytes	Custom tokens for tokenizer.	Uploaded
config.json	860 Bytes	Configuration for the model.	Uploaded
generation_config.json	281 Bytes	Configuration for text generation.	Uploaded
merges.txt	1.82 MB	Byte-pair encoding (BPE) merge rules.	Uploaded
pytorch_model-00001-of-00004.bin	4.88 GB	Model weights (split part 1).	Uploaded (LFS)
pytorch_model-00002-of-00004.bin	4.93 GB	Model weights (split part 2).	Uploaded (LFS)
pytorch_model-00003-of-00004.bin	4.33 GB	Model weights (split part 3).	Uploaded (LFS)
pytorch_model-00004-of-00004.bin	1.09 GB	Model weights (split part 4).	Uploaded (LFS)
pytorch_model.bin.index.json	28.1 kB	Index file for model weights.	Uploaded
special_tokens_map.json	644 Bytes	Map of special tokens for tokenizer.	Uploaded
tokenizer.json	11.4 MB	Tokenizer data in JSON format.	Uploaded (LFS)
tokenizer_config.json	7.73 kB	Tokenizer configuration file.	Uploaded
vocab.json	2.78 MB	Vocabulary for tokenizer.	Uploaded

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Key Features:

1. Prompt Refinement:
   Designed to enhance input prompts by rephrasing, clarifying, and optimizing for more precise outcomes.

2. Instruction Following:
   Accurately follows complex user instructions for various generation tasks, including creative writing, summarization, and question answering.

3. Customization and Fine-Tuning:
   Incorporates datasets specifically curated for prompt optimization, enabling seamless adaptation to specific user needs.

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Training Details:

• Base Model: Qwen2.5-7B-Instruct
• Datasets Used for Fine-Tuning:
  • gokaygokay/prompt-enhancer-dataset: Focuses on prompt engineering with 17.9k samples.
  • gokaygokay/prompt-enhancement-75k: Encompasses a wider array of prompt styles with 73.2k samples.
  • prithivMLmods/Prompt-Enhancement-Mini: A compact dataset (1.16k samples) for iterative refinement.

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Capabilities:

• Prompt Optimization:
  Automatically refines and enhances user-input prompts for better generation results.

• Instruction-Based Text Generation:
  Supports diverse tasks, including:

  • Creative writing (stories, poems, scripts).
  • Summaries and paraphrasing.
  • Custom Q&A systems.

• Efficient Fine-Tuning:
  Adaptable to additional fine-tuning tasks by leveraging the model's existing high-quality instruction-following capabilities.

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Usage Instructions:

1. Setup:

   • Ensure all necessary model files, including shards, tokenizer configurations, and index files, are downloaded and placed in the correct directory.

2. Load Model:
   Use PyTorch or Hugging Face Transformers to load the model and tokenizer. Ensure pytorch_model.bin.index.json is correctly set for efficient shard-based loading.

3. Customize Generation:
   Adjust parameters in generation_config.json to control aspects such as temperature, top-p sampling, and maximum sequence length.

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