---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-14B-Instruct
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: `0.5.0`
```yaml
base_model: Qwen/Qwen2.5-14B-Instruct

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: output.jsonl
    type: alpaca

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: axolotl_gmatrix
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: 0.00006
train_on_inputs:
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience: 4
local_rank:
logging_steps: 100
xformers_attention:
flash_attention: true
warmup_ratio: 0.05
#warmup_steps: 100
eval_steps: 100
save_steps: 100
save_total_limit: 2
eval_sample_packing:
debug:
deepspeed:
weight_decay: 0.01
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: 0.0875

## 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: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Use OptimizerNames.ADAMW_HF 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: 162
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0009 | 1    | 0.4243          |
| 0.2465        | 0.0923 | 100  | 0.1338          |
| 0.0425        | 0.1847 | 200  | 0.1110          |
| 0.0333        | 0.2770 | 300  | 0.1051          |
| 0.0319        | 0.3693 | 400  | 0.0933          |
| 0.0257        | 0.4617 | 500  | 0.0886          |
| 0.0245        | 0.5540 | 600  | 0.0898          |
| 0.0262        | 0.6464 | 700  | 0.0889          |
| 0.025         | 0.7387 | 800  | 0.0827          |
| 0.0221        | 0.8310 | 900  | 0.0813          |
| 0.0207        | 0.9234 | 1000 | 0.0901          |
| 0.0219        | 1.0157 | 1100 | 0.0878          |
| 0.0132        | 1.1080 | 1200 | 0.0890          |
| 0.0154        | 1.2004 | 1300 | 0.0875          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.3