diicell/qwen3-4b-instruct-2507-geogpt-sft-ru
The diicell/qwen3-4b-instruct-2507-geogpt-sft-ru is a 4 billion parameter instruction-tuned language model developed by diicell, fine-tuned from unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit. It features a 32768 token context length and was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for general instruction-following tasks, leveraging its efficient training methodology for practical applications.
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Overview
The diicell/qwen3-4b-instruct-2507-geogpt-sft-ru is a 4 billion parameter instruction-tuned model developed by diicell. It is fine-tuned from the unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit base model and utilizes a substantial 32768 token context window. This model was specifically trained for efficiency, leveraging the Unsloth library and Huggingface's TRL library, which facilitated a 2x faster training process.
Key Capabilities
- Instruction Following: Designed to accurately follow a wide range of user instructions.
- Efficient Training: Benefits from Unsloth's optimizations, allowing for quicker fine-tuning and iteration.
- Extended Context: Supports a 32768 token context length, enabling processing of longer inputs and maintaining conversational coherence over extended interactions.
Good For
- Applications requiring a compact yet capable instruction-tuned model.
- Scenarios where rapid fine-tuning and deployment are critical.
- Tasks benefiting from a large context window, such as summarization of long documents or complex multi-turn conversations.