136 lines
3.6 KiB
Markdown
136 lines
3.6 KiB
Markdown
---
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library_name: transformers
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base_model: Qwen/Qwen3-8B
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tags:
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- generated_from_trainer
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datasets:
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- laion/Sera-4.6-Lite-T2-v4-1000
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model-index:
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- name: e/data1/datasets/playground/ot-baf/checkpoints/sera-v4-1000-axolotl__Qwen3-8B-v7
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<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)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.16.0.dev0`
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```yaml
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# Sera v6 — scale data 316→1000 + num_epochs 3→6.
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#
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# Background: Sera v3 (316 rows × 6 epochs, SLURM 391242) passed turn-1 cleanly
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# but collapsed at turn-3+ (degenerate tokens, 4.4.4.4… or for-the-for-the…)
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# once a tool observation >~20 KB entered context. Greedy decoding didn't save
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# it, so the root cause is under-training rather than sampling. See
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# /Users/benjaminfeuer/Documents/notes/ot-agent/sera_braces_diagnosis.md for
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# evidence (per-token probe + turn-3 replay).
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#
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# v6 = F3 fix: 3× more rows to give the model enough updates to stay stable
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# in long multi-turn contexts.
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base_model: Qwen/Qwen3-8B
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deepspeed: /e/scratch/jureap59/feuer1/code/axolotl/deepspeed_configs/zero3_bf16.json
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load_in_8bit: false
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load_in_4bit: false
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chat_template: tokenizer_default
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datasets:
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- laion/Sera-4.6-Lite-T2-v4-1000
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type: chat_template
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field_messages: messages
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ds_type: json
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message_field_training: train
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dataset_prepared_path: /e/data1/datasets/playground/ot-baf/axolotl_dataset_cache/sera-v4-1000-v7
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output_dir: /e/data1/datasets/playground/ot-baf/checkpoints/sera-v4-1000-axolotl__Qwen3-8B-v7
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sequence_len: 32768
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name: sera-v4-1000-axolotl__Qwen3-8B-v7
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wandb_log_model:
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gradient_accumulation_steps: 8
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micro_batch_size: 1
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num_epochs: 12
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optimizer: adamw_torch
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lr_scheduler: cosine
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learning_rate: 1e-5
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adam_beta1: 0.9
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adam_beta2: 0.95
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bf16: auto
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tf32: false
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gradient_checkpointing: true
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activation_offloading: true
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resume_from_checkpoint:
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logging_steps: 1
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flash_attention: true
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loss_watchdog_threshold: 5.0
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loss_watchdog_patience: 3
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warmup_ratio: 0.1875
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evals_per_epoch: 0
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save_strategy: epoch
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weight_decay: 0.01
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max_grad_norm: 1.0
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special_tokens:
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```
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</details><br>
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# e/data1/datasets/playground/ot-baf/checkpoints/sera-v4-1000-axolotl__Qwen3-8B-v7
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This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the /e/data1/datasets/playground/ot-baf/hf_hub/datasets--laion--Sera-4.6-Lite-T2-v4-1000/snapshots/310c2661cea97bd8eb283374416193b64733fffb/sera-4.6-lite-t2_v4_1000.jsonl dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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- total_eval_batch_size: 4
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 40
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- training_steps: 218
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### Training results
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### Framework versions
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- Transformers 5.5.0
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- Pytorch 2.9.1+cu130
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- Datasets 4.5.0
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- Tokenizers 0.22.2
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