sebastian221-art/bell-motor
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:May 4, 2026Architecture:Transformer Warm
Bell-motor is a 7.6 billion parameter instruction-tuned causal language model developed by sebastian221-art. It is a fine-tuned version of unsloth/Qwen2.5-7B-Instruct, trained using the TRL framework. This model is designed for general text generation tasks, leveraging its 32768-token context length for comprehensive understanding and response generation.
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Overview
bell-motor is a 7.6 billion parameter instruction-tuned language model developed by sebastian221-art. It is built upon the unsloth/Qwen2.5-7B-Instruct architecture and has been fine-tuned using the TRL library.
Key Capabilities
- Instruction Following: Optimized for generating responses based on user instructions.
- Text Generation: Capable of producing coherent and contextually relevant text for a variety of prompts.
- Extended Context: Benefits from a 32768-token context window, allowing for processing and generating longer sequences of text.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL framework. The training environment utilized specific versions of key libraries:
- TRL: 0.24.0
- Transformers: 5.5.0
- PyTorch: 2.10.0+cu128
- Datasets: 4.3.0
- Tokenizers: 0.22.2
Good For
- General-purpose conversational AI.
- Generating creative text or answering open-ended questions.
- Applications requiring a model with a substantial context window for detailed interactions.