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Model: laion/Sera-4.6-Lite-T2-v4-1000-axolotl__Qwen3-8B-v6
Source: Original Platform

README.md

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+---
+library_name: transformers
+base_model: Qwen/Qwen3-8B
+tags:
+- generated_from_trainer
+# datasets: (stripped — invalid local path)
+_datasets_:
+- laion/Sera-4.6-Lite-T2-v4-1000
+model-index:
+- name: e/data1/datasets/playground/ot-baf/checkpoints/sera-v4-1000-axolotl__Qwen3-8B-v6
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.16.0.dev0`
+```yaml
+# Sera v6 — scale data 316→1000 + num_epochs 3→6.
+#
+# Background: Sera v3 (316 rows × 6 epochs, SLURM 391242) passed turn-1 cleanly
+# but collapsed at turn-3+ (degenerate tokens, 4.4.4.4… or for-the-for-the…)
+# once a tool observation >~20 KB entered context. Greedy decoding didn't save
+# it, so the root cause is under-training rather than sampling. See
+# /Users/benjaminfeuer/Documents/notes/ot-agent/sera_braces_diagnosis.md for
+# evidence (per-token probe + turn-3 replay).
+#
+# v6 = F3 fix: 3× more rows to give the model enough updates to stay stable
+# in long multi-turn contexts.
+
+base_model: Qwen/Qwen3-8B
+deepspeed: /e/scratch/jureap59/feuer1/code/axolotl/deepspeed_configs/zero3_bf16.json
+
+load_in_8bit: false
+load_in_4bit: false
+
+chat_template: tokenizer_default
+# datasets: (stripped — invalid local path)
+_datasets_:
+- laion/Sera-4.6-Lite-T2-v4-1000
+    type: chat_template
+    field_messages: messages
+    ds_type: json
+    message_field_training: train
+
+dataset_prepared_path: /e/data1/datasets/playground/ot-baf/axolotl_dataset_cache/sera-v4-1000-v6
+output_dir: /e/data1/datasets/playground/ot-baf/checkpoints/sera-v4-1000-axolotl__Qwen3-8B-v6
+
+sequence_len: 32768
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name: sera-v4-1000-axolotl__Qwen3-8B-v6
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 6
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 1e-5
+adam_beta1: 0.9
+adam_beta2: 0.95
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+activation_offloading: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+loss_watchdog_threshold: 5.0
+loss_watchdog_patience: 3
+
+warmup_ratio: 0.1875
+evals_per_epoch: 0
+save_strategy: epoch
+
+weight_decay: 0.01
+max_grad_norm: 1.0
+special_tokens:
+
+```
+
+</details><br>
+
+# e/data1/datasets/playground/ot-baf/checkpoints/sera-v4-1000-axolotl__Qwen3-8B-v6
+
+- laion/Sera-4.6-Lite-T2-v4-1000
+
+## 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: 1e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 32
+- total_eval_batch_size: 4
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 20
+- training_steps: 109
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 5.5.0
+- Pytorch 2.9.1+cu130
+- Datasets 4.5.0
+- Tokenizers 0.22.2