Model Overview

This model, laion/syh-r2eg-askl-glm_4-7_trac_jupi_-gfi-swes-rand-filt-10K_glm_4-7_trac_jupi_32B, is a 32 billion parameter language model derived from the Qwen/Qwen3-32B base architecture. It has been fine-tuned using two distinct datasets: one related to exp-syh-r2egym-askllm-constrained_glm_4.7_traces_jupiter_cleaned and another from exp-gfi-swesmith-random-filtered-10K_glm_4.7_traces_jupiter. The fine-tuning process involved 7 epochs with a learning rate of 4e-05 and a total training batch size of 32 across 16 GPUs.

Training Details

• Base Model: Qwen/Qwen3-32B
• Parameter Count: 32 billion
• Context Length: 32768 tokens
• Datasets Used:
  • exp-syh-r2egym-askllm-constrained_glm_4.7_traces_jupiter_cleaned
  • exp-gfi-swesmith-random-filtered-10K_glm_4.7_traces_jupiter
• Key Hyperparameters:
  • Learning Rate: 4e-05
  • Optimizer: ADAMW_TORCH_FUSED
  • Number of Epochs: 7.0
  • Gradient Accumulation Steps: 2

Potential Use Cases

Given its fine-tuning on specific trace-related datasets, this model is likely optimized for tasks that involve:

• Processing or generating text based on structured traces.
• Understanding or responding within constrained language environments.
• Applications requiring specialized knowledge derived from the training data's domain.