DCAgent/d1_trace_hints_top4_seq_glm47 is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. This model is specifically adapted for sequence generation based on trace hints, leveraging a specialized dataset for its training. It is designed to process and generate content related to specific trace hint sequences, making it suitable for tasks requiring contextual understanding and generation within defined data traces. The model's fine-tuning focuses on improving performance for applications that benefit from detailed sequence-based information processing.
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Model Overview
DCAgent/d1_trace_hints_top4_seq_glm47 is an 8 billion parameter language model, fine-tuned from the base Qwen/Qwen3-8B architecture. This model has undergone specialized training on the /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--d1_trace_hints_top4_seq_glm47_traces dataset, indicating a focus on tasks involving trace hints and sequence generation.
Key Characteristics
- Base Model: Qwen/Qwen3-8B
- Parameter Count: 8 billion
- Context Length: 32768 tokens
- Fine-tuning Focus: Optimized for processing and generating content based on specific trace hint sequences.
Training Details
The model was trained with a learning rate of 4e-05, a batch size of 1 per device across 16 GPUs (totaling 16), and utilized the AdamW_Torch_Fused optimizer. A cosine learning rate scheduler with a 0.1 warmup ratio was applied over 7 epochs. The training environment included Transformers 4.57.6, Pytorch 2.9.1+cu130, Datasets 4.7.0, and Tokenizers 0.22.2.
Potential Use Cases
This model is likely best suited for applications that require:
- Generating sequences or continuations based on provided trace hints.
- Tasks involving the analysis and synthesis of structured, sequential data.
- Scenarios where understanding and replicating patterns within specific data traces are crucial.