base_model: Qwen/Qwen3-4B
hub_model_id: minpeter/LoRA-Qwen3-4b-v1-iteration-01-sf-apigen-01

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: minpeter/apigen-mt-5k-friendli
    data_files:
      - train.jsonl
      - test.jsonl
    type: chat_template
    roles_to_train: ["assistant"]
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
    shards: 3
chat_template: chatml

dataset_prepared_path: last_run_prepared

output_dir: ./output

adapter: lora
lora_model_dir:

sequence_len: 8192
pad_to_sequence_len: true
sample_packing: true

val_set_size: 0.05
eval_sample_packing: true
evals_per_epoch: 3

lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project: "axolotl"
wandb_entity: "kasfiekfs-e"
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
tf32: true

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 10
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:

LoRA-Qwen3-4b-v1-iteration-01-sf-apigen-01

This model is a fine-tuned version of Qwen/Qwen3-4B on the minpeter/apigen-mt-5k-friendli dataset. It achieves the following results on the evaluation set:

Loss: 0.2285

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Use OptimizerNames.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: 10
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.4432	0.0069	1	1.0528
0.3253	0.3322	48	0.2922
0.4198	0.6644	96	0.2638
0.4426	0.9965	144	0.2449
0.2287	1.3253	192	0.2340
0.1526	1.6574	240	0.2299
0.268	1.9896	288	0.2285

Framework versions

PEFT 0.15.2
Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.1
Tokenizers 0.21.1

minpeter/LoRA-Qwen3-4b-v1-iteration-02-sf-apigen-01

LoRA-Qwen3-4b-v1-iteration-01-sf-apigen-01

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

minpeter/
LoRA-Qwen3-4b-v1-iteration-02-sf-apigen-01