stefra/llama_pe_joint_merged

TEXT GENERATIONConcurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 9, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

stefra/llama_pe_joint_merged is an 8 billion parameter Llama 3.1 instruction-tuned causal language model developed by stefra. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is optimized for efficient deployment and performance, leveraging its Llama 3.1 base for general language understanding and generation tasks.

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Model Overview

stefra/llama_pe_joint_merged is an 8 billion parameter instruction-tuned language model based on the Meta-Llama-3.1-8B-Instruct architecture. Developed by stefra, this model was fine-tuned using a combination of Unsloth and Huggingface's TRL library. A key differentiator of this model is its training efficiency, having been trained 2x faster due to the methodologies employed.

Key Characteristics

  • Base Model: Fine-tuned from unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit.
  • Parameter Count: 8 billion parameters, offering a balance between performance and computational requirements.
  • Training Efficiency: Utilizes Unsloth for significantly faster training (2x speedup).
  • Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs and generating more coherent extended outputs.

Use Cases

This model is suitable for a variety of general-purpose language tasks, benefiting from its Llama 3.1 foundation and efficient fine-tuning. Its optimized training process suggests potential for applications where rapid iteration and deployment of instruction-tuned models are beneficial.

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