djalal548/pgabl-ft-nama-siswa
TEXT GENERATIONConcurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 2, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The djalal548/pgabl-ft-nama-siswa is a 0.5 billion parameter Qwen2.5-Instruct model, finetuned by djalal548. This model was optimized for faster training using Unsloth and Huggingface's TRL library, building upon the unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit base. With a context length of 32768 tokens, it is designed for efficient instruction-following tasks.
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Model Overview
The djalal548/pgabl-ft-nama-siswa is a 0.5 billion parameter instruction-tuned language model developed by djalal548. It is finetuned from the unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit base model, leveraging the Qwen2.5 architecture.
Key Characteristics
- Architecture: Based on the Qwen2.5-Instruct family.
- Parameter Count: 0.5 billion parameters, making it a compact and efficient model.
- Training Optimization: This model was finetuned significantly faster using the Unsloth library in conjunction with Huggingface's TRL library. This indicates a focus on efficient and accelerated training methodologies.
- Context Length: Supports a substantial context window of 32768 tokens.
Good For
- Instruction Following: Suitable for tasks requiring the model to follow specific instructions, given its instruction-tuned nature.
- Resource-Constrained Environments: Its smaller parameter count (0.5B) makes it a good candidate for deployment in environments with limited computational resources.
- Rapid Prototyping: The use of Unsloth for faster finetuning suggests it could be beneficial for quick experimentation and iteration on instruction-tuned models.