Ahatsham/Llama-3-8B-Instruct_Planning_Feedback_oldaug_v2

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kPublished:Apr 1, 2026Architecture:Transformer Cold

Ahatsham/Llama-3-8B-Instruct_Planning_Feedback_oldaug_v2 is an 8 billion parameter instruction-tuned language model, fine-tuned from the Llama 3 architecture. This model is designed for general language understanding and generation tasks, leveraging its instruction-following capabilities. With an 8192 token context length, it is suitable for processing moderately long inputs and generating coherent responses.

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Model Overview

Ahatsham/Llama-3-8B-Instruct_Planning_Feedback_oldaug_v2 is an 8 billion parameter instruction-tuned model based on the Llama 3 architecture. This model is designed to follow instructions effectively, making it suitable for a variety of natural language processing tasks.

Key Characteristics

  • Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports an 8192 token context window, allowing for the processing of substantial input lengths.
  • Instruction-Tuned: Optimized for understanding and executing user instructions, enhancing its utility in interactive applications.

Intended Use Cases

This model is broadly applicable for tasks requiring instruction following and general text generation. While specific use cases are not detailed in the provided model card, its instruction-tuned nature suggests suitability for:

  • Conversational AI: Engaging in dialogue and responding to user queries.
  • Content Generation: Creating various forms of text based on prompts.
  • Text Summarization: Condensing longer texts into concise summaries.
  • Question Answering: Providing answers to questions based on given context.

Limitations and Recommendations

The model card indicates that more information is needed regarding potential biases, risks, and specific limitations. Users are advised to be aware of these inherent aspects of large language models and to exercise caution, especially in sensitive applications. Further recommendations will be provided as more details become available.

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