lodrick-the-lafted/Platyboros-Instruct-7B

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:8kPublished:Feb 21, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Cold

Platyboros-Instruct-7B by lodrick-the-lafted is a 7 billion parameter instruction-tuned language model built upon Mistral-7B-Instruct-v0.2. It was fine-tuned using a combination of the jondurbin/airoboros-3.2 and garage-bAInd/Open-Platypus datasets in Alpaca format. This model is designed for general instruction-following tasks, demonstrating competitive performance across various benchmarks with an average score of 64.19 on the Open LLM Leaderboard.

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Platyboros-Instruct-7B: An Instruction-Tuned Mistral Model

Platyboros-Instruct-7B is a 7 billion parameter instruction-following language model developed by lodrick-the-lafted. It is based on the robust Mistral-7B-Instruct-v0.2 architecture and has been further fine-tuned using a blend of two prominent datasets: jondurbin/airoboros-3.2 and [garage-bAInd/Open-Platypus]. This training approach leverages the Alpaca instruction format, enhancing the model's ability to understand and execute diverse user commands.

Key Capabilities & Performance

This model is optimized for general-purpose instruction following, making it suitable for a wide range of conversational and task-oriented applications. Its performance has been evaluated on the Open LLM Leaderboard, where it achieved an average score of 64.19. Specific benchmark results include:

  • AI2 Reasoning Challenge (25-Shot): 57.76
  • HellaSwag (10-Shot): 82.59
  • MMLU (5-Shot): 62.05
  • TruthfulQA (0-shot): 60.92
  • Winogrande (5-shot): 78.14
  • GSM8k (5-shot): 43.67

Prompt Format

Platyboros-Instruct-7B supports both the default Mistral-Instruct tags and the Alpaca format for prompts. The tokenizer is configured to default to the Alpaca format, providing flexibility for integration into existing workflows.

Good For

  • General instruction-following tasks
  • Applications requiring a 7B parameter model with a 8192 token context length
  • Developers familiar with Mistral-Instruct or Alpaca prompting styles

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p