DCAgent/c1_kimi_k2.5
DCAgent/c1_kimi_k2.5 is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. This model is specifically adapted using a thinking preprocessed dataset, suggesting an optimization for internal reasoning or complex task execution. It is designed for applications requiring advanced cognitive processing capabilities within a 32768-token context window.
Loading preview...
Overview
DCAgent/c1_kimi_k2.5 is an 8 billion parameter language model, fine-tuned from the Qwen/Qwen3-8B architecture. This model has undergone specialized training on a "thinking preprocessed" dataset, indicating a focus on enhancing its internal reasoning processes and ability to handle complex, multi-step tasks. The fine-tuning process involved specific hyperparameters, including a learning rate of 4e-05 and 7 epochs of training, utilizing a multi-GPU setup.
Key Characteristics
- Base Model: Qwen/Qwen3-8B
- Parameter Count: 8 billion
- Context Length: 32768 tokens
- Fine-tuning Data: Utilizes a unique "thinking preprocessed" dataset, suggesting an emphasis on cognitive or reasoning-intensive tasks.
Training Details
The model was trained with a learning rate of 4e-05, a total batch size of 16 across 16 devices, and employed an AdamW optimizer with a cosine learning rate scheduler. The training spanned 7 epochs, leveraging Transformers 4.57.6 and PyTorch 2.9.1+cu130.
Potential Use Cases
Given its fine-tuning on a "thinking preprocessed" dataset, this model is likely optimized for:
- Complex problem-solving
- Reasoning and logical inference tasks
- Applications requiring structured thought processes