DCAgent/c1_top4_seq_glm46
DCAgent/c1_top4_seq_glm46 is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. This model is specifically trained on the DCAgent/c1_top4_seq_glm46_traces dataset, suggesting an optimization for sequential decision-making or agent-based tasks. With a context length of 32768 tokens, it is designed for applications requiring extensive contextual understanding in specialized domains.
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Model Overview
This model, DCAgent/c1_top4_seq_glm46, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has been specifically fine-tuned on a unique dataset, /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--c1_top4_seq_glm46_traces/snapshots/6e70e5cd05b2eb737cf2fe7c0b9cc2aab8f35e31_thinking_preprocessed, indicating a specialization in processing and generating sequences related to agent-based interactions or complex decision-making processes.
Training Details
The model underwent 7 epochs of training using a learning rate of 4e-05 and an AdamW optimizer. It leveraged a multi-GPU setup with 16 devices, achieving a total training batch size of 16. The training procedure utilized a cosine learning rate scheduler with a warmup ratio of 0.1. This fine-tuning process aims to adapt the base Qwen3-8B model for specific sequential tasks, likely involving complex reasoning or trace analysis.
Potential Use Cases
Given its specialized training data, this model is likely well-suited for:
- Agent-based simulations: Generating or analyzing sequences of actions and thoughts within an agent's environment.
- Sequential decision-making: Tasks requiring understanding and prediction of multi-step processes.
- Trace analysis: Interpreting and summarizing complex operational or logical traces.