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axolotl-qwen-cn-3e-4-lora

PEFT
TensorBoard
Safetensors
qwen2
Generated from Trainer
8-bit precision
bitsandbytes
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axolotl-qwen-cn-3e-4-lora
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---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: Qwen/Qwen1.5-7B
model-index:
- name: home/yujia/home/CN_Hateful/trained_models/qwen/CN/toxi/3e-4/
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
# base_model: Qwen/Qwen-7B
base_model: Qwen/Qwen1.5-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

trust_remote_code: true

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  # - path: mhenrichsen/alpaca_2k_test
  - path: /home/yujia/home/CN_Hateful/train_toxiCN_cn.json
  # - path: /home/yujia/home/CN_Hateful/train_toxiCN.json
  # - path: /home/yujia/home/CN_Hateful/train.json
  # - path: /home/yujia/home/CN_Hateful/train_cn.json
    ds_type: json
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: /home/yujia/home/CN_Hateful/trained_models/qwen/CN/toxi/3e-4/
# output_dir: /home/yujia/home/CN_Hateful/trained_models/qwen/toxi/1e-5/
# output_dir: /home/yujia/home/CN_Hateful/trained_models/qwen/cold/3e-4/
# output_dir: /home/yujia/home/CN_Hateful/trained_models/qwen/CN/cold/3e-4/

sequence_len: 256  # supports up to 8192
sample_packing: false
pad_to_sequence_len:

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0003

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 20
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# home/yujia/home/CN_Hateful/trained_models/qwen/CN/toxi/3e-4/

This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1261

## 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3182        | 0.0   | 1    | 3.3363          |
| 0.0729        | 0.25  | 142  | 0.0904          |
| 0.0278        | 0.5   | 284  | 0.0789          |
| 0.1243        | 0.75  | 426  | 0.0724          |
| 0.0808        | 1.0   | 568  | 0.0936          |
| 0.0474        | 1.26  | 710  | 0.0780          |
| 0.0379        | 1.51  | 852  | 0.0863          |
| 0.0545        | 1.76  | 994  | 0.0793          |
| 0.0044        | 2.01  | 1136 | 0.0819          |
| 0.0021        | 2.26  | 1278 | 0.1127          |
| 0.0258        | 2.51  | 1420 | 0.1188          |
| 0.0429        | 2.76  | 1562 | 0.1261          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0