pihu21057w/jp
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Mar 3, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The pihu21057w/jp is an 8 billion parameter instruction-tuned causal language model, finetuned by pihu21057w from unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit. This model leverages Unsloth for accelerated training, making it a fast and efficient option for various natural language processing tasks. With a 32K context length, it is suitable for applications requiring processing of longer inputs.
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Model Overview
The pihu21057w/jp model is an 8 billion parameter instruction-tuned language model, developed by pihu21057w. It is finetuned from the unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit base model, indicating its foundation in the Llama 3.1 architecture.
Key Characteristics
- Efficient Training: This model was trained using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process. This efficiency can translate to quicker iteration and deployment for developers.
- Base Model: Finetuned from
unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit, suggesting it inherits the capabilities and instruction-following characteristics of the Llama 3.1 series. - Context Length: The model supports a context length of 32,768 tokens, allowing it to handle substantial input sizes for complex tasks.
Potential Use Cases
- Instruction Following: Given its instruction-tuned nature, it is well-suited for tasks requiring precise adherence to prompts and instructions.
- Applications requiring efficient models: The use of Unsloth for training implies an optimized and potentially resource-friendly model, beneficial for deployment in environments with computational constraints.
- General NLP tasks: Its Llama 3.1 foundation makes it versatile for a wide range of natural language processing applications.