didula-wso2/exp_24_0_clsft_16bit_vllm
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Dec 22, 2025License:apache-2.0Architecture:Transformer Open Weights Cold
The didula-wso2/exp_24_0_clsft_16bit_vllm is a 7.6 billion parameter Qwen2 model developed by didula-wso2, featuring a 131,072 token context length. This model was finetuned from didula-wso2/exp_23_emb_grpo_checkpoint_1000_16bit_vllm and optimized for training speed using Unsloth and Huggingface's TRL library. It is designed for classification tasks, leveraging its efficient training methodology for rapid deployment.
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Model Overview
The didula-wso2/exp_24_0_clsft_16bit_vllm is a 7.6 billion parameter Qwen2 model developed by didula-wso2. It boasts a substantial context length of 131,072 tokens, making it suitable for processing extensive inputs.
Key Characteristics
- Architecture: Based on the Qwen2 model family.
- Parameter Count: 7.6 billion parameters.
- Context Length: Supports a large context window of 131,072 tokens.
- Training Efficiency: Finetuned using Unsloth and Huggingface's TRL library, resulting in 2x faster training compared to standard methods.
- Origin: Finetuned from the
didula-wso2/exp_23_emb_grpo_checkpoint_1000_16bit_vllmcheckpoint.
Use Cases
This model is particularly well-suited for:
- Classification tasks: Its finetuning process suggests an optimization for specific classification applications.
- Applications requiring large context: The extensive 131,072 token context window allows for processing and understanding long documents or complex conversational histories.
- Efficient deployment: Models trained with Unsloth are often optimized for faster inference and reduced resource consumption.