magnifi/magnifi-classifier-01-05-search-agent-3-epochs-3k-unknown-errors
010
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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"/> <details><summary>See axolotl config</summary>
axolotl version: 0.13.0.dev0
base_model: Qwen/Qwen3-4B-Instruct-2507
# Automatically upload checkpoint and final model to HF
hub_model_id: Tifin-Sage/magnifi-classifier-01-05-search-agent-3-epochs-3k-unknown-errors
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: qwen3
datasets:
- path: Tifin-Sage/magnify-search-relabel-01-02
type: chat_template
field_messages: messages
message_property_mappings:
role: role
content: content
val_set_size: 0.1
output_dir: /workspace/data/outputs/qwen3-4B/fft/
dataset_prepared_path: /workspace/data/datasets_prepared/magnifi-classifier-01-05-search-agent-3-epochs-3k-unknown-errors
sequence_len: 16000
sample_packing: true
eval_sample_packing: true
wandb_project: sage-classifier
wandb_entity:
wandb_watch:
wandb_name: magnifi-classifier-01-05-search-agent-3-epochs-3k-unknown-errors
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 3
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:
# save_first_step: true # uncomment this to validate checkpoint saving works with your config</details><br>
magnifi-classifier-01-05-search-agent-3-epochs-3k-unknown-errors
This model is a fine-tuned version of Qwen/Qwen3-4B-Instruct-2507 on the Tifin-Sage/magnify-search-relabel-01-02 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3275
- Ppl: 1.3875
- Memory/max Active (gib): 36.15
- Memory/max Allocated (gib): 36.15
- Memory/device Reserved (gib): 56.9
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
- trainbatchsize: 1
- evalbatchsize: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- totaltrainbatch_size: 2
- totalevalbatch_size: 2
- optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 70
- training_steps: 708
Training results
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
