See axolotl config
axolotl version: 0.12.2
base_model: Qwen/Qwen3-0.6B
trust_remote_code: true
strict: false
chat_template: qwen3
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
datasets:
- path: ./Dataset/dataset_bpln.jsonl
type: chat_template
field_messages: conversations
message_property_mappings:
role: from
content: value
roles:
user: ["human"]
assistant: ["gpt"]
system: ["system"]
dataset_prepared_path: ./process
val_set_size: 0.01
output_dir: ./outputs/out
sequence_len: 256
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: BPLN
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
load_in_8bit: false
load_in_4bit: false
gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 8e-5
weight_decay: 0.0
warmup_ratio: 0.05
bf16: true
fp16: false
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
flash_attention: false
logging_steps: 1
evals_per_epoch: 1
saves_per_epoch: 1
save_total_limit: 2
special_tokens:
eos_token: "<|im_end|>"
outputs/out
This model is a fine-tuned version of Qwen/Qwen3-0.6B on the ./Dataset/dataset_bpln.jsonl dataset. It achieves the following results on the evaluation set:
- Loss: 1.5580
- Memory/max Mem Active(gib): 1.44
- Memory/max Mem Allocated(gib): 1.44
- Memory/device Mem Reserved(gib): 1.49
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: 8e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 4
- training_steps: 81
Training results
| Training Loss | Epoch | Step | Validation Loss | Mem Active(gib) | Mem Allocated(gib) | Mem Reserved(gib) |
|---|---|---|---|---|---|---|
| No log | 0 | 0 | 1.8057 | 1.17 | 1.17 | 1.19 |
| 1.7214 | 0.9818 | 27 | 1.5839 | 1.44 | 1.44 | 1.49 |
| 1.7178 | 1.9455 | 54 | 1.5639 | 1.44 | 1.44 | 1.49 |
| 1.6562 | 2.9091 | 81 | 1.5580 | 1.44 | 1.44 | 1.49 |
Framework versions
- PEFT 0.17.0
- Transformers 4.55.2
- Pytorch 2.5.1+cu121
- Datasets 4.0.0
- Tokenizers 0.21.4
- Downloads last month
- -