yezg/myqwenmodel

See axolotl config

axolotl version: 0.10.0.dev0

base_model: Qwen/Qwen2.5-coder-3B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
chat_template: qwen_25

adapter: qlora
lora_r: 8
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - c_attn
  - c_proj
  - w1
  - w2
  - q_proj
  - v_proj
  - k_proj
  - o_proj

load_in_4bit: true
bnb_4bit_compute_dtype: float16
bnb_4bit_use_double_quant: true
bnb_4bit_quant_type: nf4

datasets:
  - path: ./datasets/generic_formatted_data.jsonl
    type: alpaca

val_set_size: 0.01
dataset_prepared_path:

sequence_len: 2048
pad_to_sequence_len: true

output_dir: ./outputs/qwen2.5-coder-3b-lora
num_epochs: 3
micro_batch_size: 2
gradient_accumulation_steps: 8
evals_per_epoch: 1
saves_per_epoch: 1
optimizer: adamw_bnb_8bit
learning_rate: 2e-5
lr_scheduler: cosine
warmup_steps: 50

gradient_checkpointing: true
fp16: true
bf16: false
tf32: true
flash_attention: true
eager_attention: false

logging_steps: 1
debug: true
wandb_project: qwen-coder
wandb_name: qwen2.5-coder-3b-lora
wandb_log_model: "false"
wandb_mode: disabled

outputs/qwen2.5-coder-3b-lora

This model is a fine-tuned version of Qwen/Qwen2.5-coder-3B on the ./datasets/generic_formatted_data.jsonl dataset. It achieves the following results on the evaluation set:

• Loss: 0.0817

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: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• total_eval_batch_size: 4
• optimizer: Use OptimizerNames.ADAMW_BNB 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: 50
• training_steps: 1375
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.0456	0.0022	1	0.9417
0.3029	1.0	459	0.1403
0.044	2.0	918	0.0817

Framework versions

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