magnifi-module-classifier-04-17-relabelled-upsampled

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<details><summary>See axolotl config</summary>

axolotl version: 0.16.0.dev0

base_model: Tifin-Sage/magnifi-classifier-01-05-search-agent-3-epochs-3k-unknown-errors
hub_model_id: Tifin-Sage/magnifi-module-classifier-04-17-relabelled-upsampled

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: qwen3
datasets:
  - path: Tifin-Sage/magnifi-module-classifier-04-17-relabelled-upsampled
    type: chat_template
    split: train
    field_messages: messages
    message_property_mappings:
      role: role
      content: content

val_set_size: 0.1

output_dir: /workspace/data/outputs/qwen3-4B/fft_magnifi-module-classifier-04-17-relabelled-upsampled/
dataset_prepared_path: /workspace/data/datasets_prepared/magnifi-module-classifier-04-17-relabelled-upsampled

sequence_len: 16000
sample_packing: true
eval_sample_packing: true


wandb_project: sage-classifier
wandb_entity:
wandb_watch:
wandb_name: magnifi-module-classifier-04-17-relabelled-upsampled
wandb_log_model: 

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-5

bf16: auto
tf32: true

resume_from_checkpoint:
logging_steps: 1

evals_per_epoch: 2
saves_per_epoch: 1

warmup_ratio: 0.1
weight_decay: 0.0
fsdp:
  - full_shard
  - auto_wrap

fsdp_config:
  fsdp_version: 2
  fsdp_offload_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_sharding_strategy: FULL_SHARD
  fsdp_reshard_after_forward: true
  fsdp_activation_checkpointing: true

special_tokens:

</details><br>

magnifi-module-classifier-04-17-relabelled-upsampled

This model is a fine-tuned version of Tifin-Sage/magnifi-classifier-01-05-search-agent-3-epochs-3k-unknown-errors on the Tifin-Sage/magnifi-module-classifier-04-17-relabelled-upsampled dataset. It achieves the following results on the evaluation set:

• Loss: 0.2227
• Ppl: 1.2494
• Memory/max Active (gib): 34.91
• Memory/max Allocated (gib): 34.91
• Memory/device Reserved (gib): 57.25

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: 2e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 2
• total_eval_batch_size: 2
• 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_steps: 47
• training_steps: 478

Training results

Framework versions

• Transformers 5.5.4
• Pytorch 2.10.0+cu128
• Datasets 4.8.4
• Tokenizers 0.22.2