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sudoping01 / bambara-asr-llm-exp1-all
Model's Last Updated: September 09 2025
text-generation
Introduction of bambara-asr-llm-exp1-all
Model Details of bambara-asr-llm-exp1-all
See axolotl config
axolotl version:
0.12.2
base_model: sudoping01/bambara-llm-exp3-merged
processor_type: AutoProcessor
hub_model_id: sudoping01/bambara-asr-llm-exp1
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
cut_cross_entropy: true
skip_prepare_dataset: true
remove_unused_columns: false
sample_packing: false
ddp: true
ddp_find_unused_parameters: true
# Template and tokens
chat_template: gemma3n
eot_tokens:
- <end_of_turn>
special_tokens:
eot_token: <end_of_turn>
datasets:
- path: instruction_dataset_asr_axolotl_format.jsonl
type: chat_template
val_set_size: 0.01
output_dir: ./outputs/bambara-gemma3n-asr-lora-exp1
adapter: lora
lora_r: 32 # Reduced from 64 for stability
lora_alpha: 64 # Reduced from 128 for stability
lora_dropout: 0.05
lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
# Sequence and batch settings - conservative for audio
sequence_len: 4096 # Reduced from 4096
pad_to_sequence_len: false
micro_batch_size: 8 # Increased: You have 8x H100s, can handle larger batches
gradient_accumulation_steps: 2
# Training parameters
num_epochs: 6 # Start with 1 epoch for testing
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-4 # Slightly higher as per research
warmup_ratio: 0.1 # Increased warmup for multimodal
weight_decay: 0.0 # Set to 0 for multimodal
bf16: true # Must be true, not auto
tf32: false
load_in_4bit: false # Keep false for quality
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
# Monitoring
logging_steps: 1 # More frequent for debugging
saves_per_epoch: 2
evals_per_epoch: 2
# ASR metrics
metrics:
- name: wer
- name: cer
bambara-asr-llm-exp1
This model is a fine-tuned version of sudoping01/bambara-llm-exp3-merged on the instruction_dataset_asr_axolotl_format.jsonl dataset. It achieves the following results on the evaluation set:
- Loss: 0.0887
- Memory/max Mem Active(gib): 18.76
- Memory/max Mem Allocated(gib): 18.76
- Memory/device Mem Reserved(gib): 19.99
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Use adamw_8bit 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: 350
- training_steps: 3508
Training results
| Training Loss | Epoch | Step | Validation Loss | Mem Active(gib) | Mem Allocated(gib) | Mem Reserved(gib) |
|---|---|---|---|---|---|---|
| No log | 0 | 0 | 2.3603 | 18.76 | 18.76 | 19.99 |
| 0.5427 | 0.5009 | 293 | 0.5847 | 18.76 | 18.76 | 19.99 |
| 0.3592 | 1.0017 | 586 | 0.4359 | 18.76 | 18.76 | 19.99 |
| 0.3552 | 1.5026 | 879 | 0.3764 | 18.76 | 18.76 | 19.99 |
| 0.2928 | 2.0034 | 1172 | 0.3247 | 18.76 | 18.76 | 19.99 |
| 0.2413 | 2.5043 | 1465 | 0.2867 | 18.76 | 18.76 | 19.99 |
| 0.2799 | 3.0051 | 1758 | 0.2314 | 18.76 | 18.76 | 19.99 |
| 0.1091 | 3.5060 | 2051 | 0.2033 | 18.76 | 18.76 | 19.99 |
| 0.115 | 4.0068 | 2344 | 0.1539 | 18.76 | 18.76 | 19.99 |
| 0.0835 | 4.5077 | 2637 | 0.1185 | 18.76 | 18.76 | 19.99 |
| 0.0722 | 5.0085 | 2930 | 0.1021 | 18.76 | 18.76 | 19.99 |
| 0.0945 | 5.5094 | 3223 | 0.0887 | 18.76 | 18.76 | 19.99 |
Framework versions
- PEFT 0.17.0
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
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Updated:March 07 2026
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