prithivMLmods/LwQ-10B-Instruct

Hugging Face
TEXT GENERATIONPricing:Input $0.431 / Cached $0.0862 / Output $1.12Concurrent Unit Cost:1Model Size:10.7BQuant:FP8Context Size:4kPublished:Jan 14, 2025License:llama3.1Architecture:Transformer0.0K Featherless Exclusive Warm

prithivMLmods/LwQ-10B-Instruct is a 10.7 billion parameter instruction-tuned generative language model based on the Llama 3.1 architecture, developed by prithivMLmods. It is specifically optimized for multilingual dialogue, mathematical reasoning, and context-based problem-solving, with a focus on following embedded instructions or keywords. This model excels at tasks requiring adherence to specific instructions and is suitable for multilingual conversational agents and structured content generation.

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LwQ-10B-Instruct: Multilingual Reasoning and Instruction Following

LwQ-10B-Instruct, or Llama with Questions, is a 10.7 billion parameter instruction-tuned model built upon the Llama 3.1 architecture. Developed by prithivMLmods, it is designed for multilingual dialogue and excels in scenarios requiring precise instruction adherence and complex reasoning. The model has undergone supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to enhance its helpfulness and safety.

Key Capabilities

  • Multilingual Dialogue: Optimized for building chatbots and virtual assistants that provide context-aware responses across various languages.
  • Instruction Following: Highly effective in tasks where adhering to specific instructions or keywords is crucial, such as task automation and guided workflows.
  • Mathematical and Logical Reasoning: Trained on synthetic reasoning datasets to handle mathematical problem-solving and logical explanations, suitable for educational applications.
  • Contextual Problem-Solving: Processes inputs with embedded instructions to solve contextually rich problems, aiding in complex decision-making.
  • Content Generation: Capable of generating high-quality content, including articles and summaries, in multiple languages.

Good For

  • Developing multilingual conversational agents and virtual assistants.
  • Applications requiring strict instruction adherence and structured output.
  • Educational platforms needing support for mathematical and logical reasoning.
  • Systems that benefit from contextual problem-solving and understanding embedded keywords.
  • Generating diverse content and summaries across different languages and domains.

Finetunes
2 models