mogun123/finally-v11
VISIONConcurrent Unit Cost:1Model Size:5.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 30, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
mogun123/finally-v11 is a 5.1 billion parameter Gemma4-based causal language model, fine-tuned by mogun123 with a 32768 token context length. This model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. It is optimized for general language tasks, leveraging its Gemma4 architecture for efficient performance.
Loading preview...
mogun123/finally-v11: A Fine-Tuned Gemma4 Model
mogun123/finally-v11 is a 5.1 billion parameter language model based on the Gemma4 architecture, featuring a substantial 32768 token context length. Developed by mogun123, this model was fine-tuned using a combination of Unsloth and Huggingface's TRL library, which facilitated a significantly faster training process.
Key Characteristics
- Base Model: Finetuned from
unsloth/gemma-4-e2b-it-unsloth-bnb-4bit, indicating a foundation in Google's Gemma series. - Efficient Training: Leverages Unsloth for 2x faster fine-tuning, suggesting optimizations for resource-efficient development.
- Parameter Count: At 5.1 billion parameters, it offers a balance between capability and computational demands.
- Context Length: A 32768 token context window allows for processing and generating longer, more coherent texts.
Potential Use Cases
- General Text Generation: Suitable for a wide range of language generation tasks due to its robust base architecture and fine-tuning.
- Applications Requiring Longer Context: The extended context length makes it ideal for tasks like summarization of lengthy documents, detailed question answering, or maintaining conversational coherence over many turns.
- Research and Development: Its origin from an Unsloth-optimized training process makes it an interesting candidate for further experimentation in efficient LLM fine-tuning.