See axolotl config
axolotl version: 0.5.0
base_model: Qwen/Qwen2.5-7B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
datasets:
- path: training_data_expanded.jsonl
type: chat_template
chat_template: chatml
field_messages: messages
message_field_role: role
message_field_content: content
output_dir: ./qwen-123go-finetuned
sequence_len: 512
sample_packing: false
pad_to_sequence_len: true
eval_sample_packing: false
micro_batch_size: 1
gradient_accumulation_steps: 4
num_epochs: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002
bf16: true
fp16: false
tf32: false
logging_steps: 5
save_steps: 50
gradient_checkpointing: true
flash_attention: false
val_set_size: 0.1
evals_per_epoch: 1
seed: 42
qwen-123go-finetuned
This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0 | 0.9985 | 337 | 0.0000 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.3
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