CooperBench/Qwen3.5-9B-cooperdata-bridge-midtrain-32k
CooperBench/Qwen3.5-9B-cooperdata-bridge-midtrain-32k is a 9 billion parameter causal language model, fine-tuned from Qwen/Qwen3.5-9B by CooperBench. This model was trained on the cooperdata-bridge-midtrain-blend dataset, utilizing a 32k token context length. It is optimized for tasks related to the specific data it was fine-tuned on, making it suitable for applications requiring specialized knowledge from that dataset.
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Model Overview
This model, CooperBench/Qwen3.5-9B-cooperdata-bridge-midtrain-32k, is a 9 billion parameter language model derived from the Qwen/Qwen3.5-9B base architecture. It has undergone fine-tuning using the CooperBench/cooperdata-bridge-midtrain-blend dataset, leveraging a substantial context window of 32,768 tokens.
Key Training Details
- Base Model: Qwen/Qwen3.5-9B
- Fine-tuning Dataset: CooperBench/cooperdata-bridge-midtrain-blend
- Context Length: 32,768 tokens
- Optimizer: AdamW_Torch_Fused
- Learning Rate: 1.5e-05
- Epochs: 1
- Frameworks: Transformers 5.10.2, Pytorch 2.12.0+cu130, Datasets 4.8.5, Tokenizers 0.22.2
Intended Use Cases
This model is specifically tailored for applications that benefit from the knowledge and patterns learned during its fine-tuning on the cooperdata-bridge-midtrain-blend dataset. Developers should consider this model for tasks where its specialized training data provides a distinct advantage over more general-purpose models, particularly those requiring a large context window for detailed information processing.