---
base_model: Qwen/Qwen2.5-14B-Instruct
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: outputs/lora-out
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: Qwen/Qwen2.5-14B-Instruct

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: output.jsonl
    type:
      field_instruction: instruction
      field_input: input
      field_output: output
      format: "<|im_start|>system\n{instruction}<|im_end|>\n<|im_start|>user\n{input}<|im_end|>\n<|im_start|>assistant\n"

special_tokens:
  bos_token:
  eos_token: "<|im_end|>"
  pad_token: "<|endoftext|>"

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/lora-out

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project: mssong_axolotl
wandb_entity: mssong
wandb_watch:
wandb_run_id: 
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 3
optimizer:
lr_scheduler: cosine
learning_rate: 0.00005
train_on_inputs:
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience: 3
local_rank:
logging_steps: 10
xformers_attention:
flash_attention: true
#warmup_ratio: 0.02
warmup_steps: 100
eval_steps: 100
save_steps: 500
save_total_limit: 2
eval_sample_packing:
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
trust_remote_code: true
```

</details><br>

# outputs/lora-out

This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co./Qwen/Qwen2.5-14B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0405

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0035 | 1    | 0.6979          |
| 0.046         | 0.3515 | 100  | 0.0793          |
| 0.0259        | 0.7030 | 200  | 0.0519          |
| 0.0242        | 1.0545 | 300  | 0.0447          |
| 0.0194        | 1.4060 | 400  | 0.0435          |
| 0.016         | 1.7575 | 500  | 0.0427          |
| 0.0097        | 2.1090 | 600  | 0.0392          |
| 0.0179        | 2.4605 | 700  | 0.0410          |
| 0.0081        | 2.8120 | 800  | 0.0405          |


### Framework versions

- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0