ShahriarFerdoush/llama2-13b-instruct-code-obf-merged-v2

TEXT GENERATIONConcurrency Cost:1Model Size:13BQuant:FP8Ctx Length:4kPublished:Mar 29, 2026Architecture:Transformer Cold

ShahriarFerdoush/llama2-13b-instruct-code-obf-merged-v2 is a 13 billion parameter instruction-tuned language model based on the Llama 2 architecture, developed by ShahriarFerdoush. This model is designed for general instruction following with a context length of 4096 tokens. Its primary strength lies in its merged capabilities, aiming for robust performance across various tasks.

Loading preview...

Overview

ShahriarFerdoush/llama2-13b-instruct-code-obf-merged-v2 is a 13 billion parameter instruction-tuned model built upon the Llama 2 architecture. Developed by ShahriarFerdoush, this model is a merged version, indicating an integration of different fine-tuning or base models to enhance its overall capabilities. It operates with a context length of 4096 tokens, allowing it to process moderately long inputs and generate coherent responses.

Key Capabilities

  • Instruction Following: Designed to understand and execute a wide range of instructions.
  • General Purpose: Aims for broad applicability across various natural language processing tasks.
  • Llama 2 Base: Benefits from the robust and well-regarded Llama 2 foundational architecture.

Training Details

The provided model card indicates that specific details regarding its development, funding, training data, and procedure are currently marked as "More Information Needed." This suggests that while the model is available, comprehensive technical documentation is still pending or not publicly disclosed within the model card.

Limitations and Recommendations

As with many models, users should be aware of potential biases, risks, and technical limitations. The model card explicitly states that more information is needed to provide specific recommendations regarding its use and to fully understand its scope and constraints. Users are advised to exercise caution and conduct their own evaluations, especially for sensitive applications, until further details are made available.