DCAgent/b1_top8
DCAgent/b1_top8 is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. This model is specifically fine-tuned on the DCAgent/b1_top8 dataset, indicating a specialization for tasks related to its training data. It leverages a 32,768 token context length, making it suitable for processing extensive inputs and generating detailed responses.
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Overview
DCAgent/b1_top8 is an 8 billion parameter language model, fine-tuned from the base Qwen/Qwen3-8B architecture. This model has been specifically trained on the /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--b1_top8/snapshots/0261a53fb1e70a7ba1767f28710756d33ed1048e_thinking_preprocessed dataset. The fine-tuning process involved a learning rate of 4e-05, a total training batch size of 16 across 16 GPUs, and 7 epochs, utilizing an AdamW optimizer with a cosine learning rate scheduler.
Key Characteristics
- Base Model: Qwen3-8B
- Parameter Count: 8 billion
- Context Length: 32,768 tokens
- Training Data: Fine-tuned on a specific dataset (
DCAgent/b1_top8_thinking_preprocessed), suggesting specialized capabilities related to this data.
Training Details
The model was trained using:
- Learning Rate: 4e-05
- Optimizer: AdamW_TORCH_FUSED
- LR Scheduler: Cosine with 0.1 warmup ratio
- Epochs: 7.0
- Frameworks: Transformers 4.57.6, Pytorch 2.9.1+cu130, Datasets 4.7.0, Tokenizers 0.22.2.
Potential Use Cases
Given its fine-tuning on a specific dataset, this model is likely best suited for tasks that align with the characteristics and domain of the DCAgent/b1_top8_thinking_preprocessed data. Developers should evaluate its performance on tasks requiring deep understanding or generation within that specialized domain.