---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c5d71538-c798-48d9-9166-6ebef0e4e19d
results: []
---
[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-0.5B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 196d8873f3b51865_train_data.json
ds_type: json
field: title_category
path: /workspace/input_data/196d8873f3b51865_train_data.json
type: completion
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: dimasik87/c5d71538-c798-48d9-9166-6ebef0e4e19d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/196d8873f3b51865_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 2
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: c5d71538-c798-48d9-9166-6ebef0e4e19d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c5d71538-c798-48d9-9166-6ebef0e4e19d
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null
```
# c5d71538-c798-48d9-9166-6ebef0e4e19d
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7831
## 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: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.4094 | 0.0000 | 1 | 5.5116 |
| 5.3517 | 0.0000 | 3 | 5.4905 |
| 5.4144 | 0.0001 | 6 | 5.2434 |
| 4.9439 | 0.0001 | 9 | 4.7831 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1