100k_baseline__Qwen3-8B

This model is a fine-tuned version of Qwen/Qwen3-8B on the /e/data1/datasets/playground/ot/hf_hub/datasets--DCAgent--swesmith-sandboxes-with_tests-gpt-5-mini-passed_glm_4.7_traces/snapshots/b9b0e0d113e9c37dd035f03644315478acc04487_thinking_preprocessed, the /e/data1/datasets/playground/ot/hf_hub/datasets--DCAgent--exp-uns-r2egym-16_8x_glm_4.7_traces_jupiter_cleaned/snapshots/f33c2e87626104d2406a7182a6a713bf0b337bdb_thinking_preprocessed, the /e/data1/datasets/playground/ot/hf_hub/datasets--DCAgent--exp-syh-r2egym-askllm-hardened_glm_4.7_traces_jupiter/snapshots/625842bb217a7168a4b563bc70dc391100b5f483_thinking_preprocessed, the /e/data1/datasets/playground/ot/hf_hub/datasets--DCAgent--exp-syh-r2egym-askllm-constrained_glm_4.7_traces_jupiter/snapshots/c6f0acf401312da7f0acba098ddc5bfc2d3abcb8_thinking_preprocessed, the /e/data1/datasets/playground/ot/hf_hub/datasets--DCAgent--exp_tas_optimal_combined_traces/snapshots/ebbeebd254227e227eae6f6f3f25dd76407c5d1c_thinking_preprocessed, the /e/data1/datasets/playground/ot/hf_hub/datasets--DCAgent--exp_tas_repetition_penalty_1.05_traces/snapshots/b4f5500e00651d5ffc7f8701f8a055d9b2b68a0a_thinking_preprocessed, the /e/data1/datasets/playground/ot/hf_hub/datasets--DCAgent--exp_tas_max_episodes_512_traces/snapshots/236c1dc9aa6d24cf77ce281b5342d93bae685832_thinking_preprocessed, the /e/data1/datasets/playground/ot/hf_hub/datasets--DCAgent--exp_tas_timeout_multiplier_8.0_traces/snapshots/cb412ce40164f838f33e77b98af549d815413346_thinking_preprocessed, the /e/data1/datasets/playground/ot/hf_hub/datasets--DCAgent--glm46-Toolscale-tasks-traces/snapshots/a8a1e7cf8edaa7730bc8ef37d9907888946e830b_thinking_preprocessed and the /e/data1/datasets/playground/ot/hf_hub/datasets--penfever--Kimi-K2T-swesmith-32ep-131k/snapshots/7e98e8f444f5bfe78b0e0a94fab0ec28d0487b6e_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: 128
• total_train_batch_size: 128
• total_eval_batch_size: 1024
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) 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 2.9.1+cu130
• Datasets 4.7.0
• Tokenizers 0.22.2