fasdqwfgzxgfqw/mjf-pki-qwen
The fasdqwfgzxgfqw/mjf-pki-qwen is a 3.1 billion parameter instruction-tuned causal language model developed by fasdqwfgzxgfqw. This model is finetuned from unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit and leverages Unsloth for accelerated training. It is optimized for tasks requiring efficient processing within a 32768 token context length, making it suitable for applications where rapid deployment and performance are key.
Loading preview...
Model Overview
The fasdqwfgzxgfqw/mjf-pki-qwen is a 3.1 billion parameter language model developed by fasdqwfgzxgfqw. It is an instruction-tuned variant, building upon the unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit base model. A key characteristic of this model is its training methodology, which utilized Unsloth and Huggingface's TRL library, enabling a reported 2x faster finetuning process.
Key Characteristics
- Parameter Count: 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended interactions.
- Training Efficiency: Benefits from Unsloth's optimization, which facilitated a significantly faster finetuning process.
- License: Distributed under the Apache-2.0 license, providing flexibility for various applications.
Potential Use Cases
This model is well-suited for applications requiring a capable instruction-following model with a large context window, particularly where training speed and resource efficiency are important considerations. Its finetuned nature suggests strong performance in tasks aligned with its instruction-following capabilities.