laion/syh-r2eg-askl-glm_4-7_trac_jupi_-gfi-swes-rand-filt-10K_glm_4-7_trac_jupi_32B

sft_exp-syh-r2eg-askl-cons_glm_4-7_trac_jupi_clea_exp-gfi-swes-rand-filt-10K_glm_4-7_trac_jupi_Q

This model is a fine-tuned version of Qwen/Qwen3-32B on the /data/cat/ws/befe330h-befe330h-otagent/huggingface/hub/datasets--DCAgent--exp-syh-r2egym-askllm-constrained_glm_4.7_traces_jupiter_cleaned/snapshots/d13cd4ded646d8380dc70005a25fadeae9836514_thinking_preprocessed and the /data/cat/ws/befe330h-befe330h-otagent/huggingface/hub/datasets--DCAgent--exp-gfi-swesmith-random-filtered-10K_glm_4.7_traces_jupiter/snapshots/cb971aef68078d7bd025e0d8c33040bba180d914_thinking_preprocessed datasets.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 4e-05
• train_batch_size: 1
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 16
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• total_eval_batch_size: 128
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 7.0

Training results

Framework versions

• Transformers 4.57.6
• Pytorch 2.9.0+cu128
• Datasets 4.4.1
• Tokenizers 0.22.2