giocorte/totem-slm-sft
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 20, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The giocorte/totem-slm-sft is a 4 billion parameter Qwen3-based causal language model developed by giocorte. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is suitable for tasks requiring a compact yet capable language model, leveraging the efficiency benefits of its training methodology.
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Model Overview
The giocorte/totem-slm-sft is a 4 billion parameter language model based on the Qwen3 architecture. It was developed by giocorte and fine-tuned using a combination of Unsloth and Huggingface's TRL library. This approach allowed for a significantly faster training process, making it an efficient option for various natural language processing tasks.
Key Characteristics
- Architecture: Qwen3-based, a robust and widely recognized model family.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Fine-tuned with Unsloth, which is designed to accelerate the training of large language models.
- Context Length: Supports a context length of 32768 tokens, allowing for processing of substantial input sequences.
Good For
- Applications requiring a compact yet capable language model.
- Scenarios where efficient fine-tuning and deployment are priorities.
- General natural language understanding and generation tasks within its parameter class.