---
base_model: Qwen/Qwen2-7B
library_name: peft
tags:
- generated_from_trainer
model-index:
- name: workspace/data/outputs/Qwen2-7B-TestFinetune-LORA
  results: []
datasets:
- NobodyExistsOnTheInternet/ToxicQAFinal
---

If I thought I had no idea what I was doing with quantization, I REALLY have no idea what I’m doing with LORA Fine Tuning...  This works in my 10 second testing, but I have no idea beyond that, nor did do anything other than asking it to do horrible things and seeing if it complied.

<!-- 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: `0.4.1`
```yaml
base_model: /workspace/data/models/Qwen2-7B
model_type: Qwen2ForCausalLM
tokenizer_type: Qwen2Tokenizer

trust_remote_code: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: NobodyExistsOnTheInternet/ToxicQAFinal
    type: sharegpt
#  - path: /workspace/data/SystemChat_filtered_sharegpt.jsonl
#    type: sharegpt
#    conversation: chatml
#  - path: /workspace/data/Opus_Instruct-v2-6.5K-Filtered-v2.json
#    type:
#      field_system: system
#      field_instruction: prompt
#      field_output: response
#      format: "[INST] {instruction} [/INST]"
#      no_input_format: "[INST] {instruction} [/INST]"
#  - path: Undi95/orthogonal-activation-steering-TOXIC
#    type:
#      field_instruction: goal
#      field_output: target
#      format: "[INST] {instruction} [/INST]"
#      no_input_format: "[INST] {instruction} [/INST]"
#    split: test
#  - path: cognitivecomputations/WizardLM_alpaca_evol_instruct_70k_unfiltered
#    type: alpaca
#    split: train

dataset_prepared_path: /workspace/data/last_run_prepared
val_set_size: 0.15
output_dir: /workspace/data/outputs/Qwen2-7B-TestFinetune-LORA

chat_template: chatml

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

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: 8
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 3e-5

train_on_inputs: false
group_by_length: true
bf16: true
fp16: false
tf32: false

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

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 2
debug:
deepspeed:
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
  pad_token: "<|endoftext|>"
  eos_token: "<|im_end|>"
```

</details><br>

# workspace/data/outputs/Qwen2-7B-TestFinetune-LORA

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0055

## 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: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1751        | 0.0169 | 1    | 1.1860          |
| 1.1007        | 0.5063 | 30   | 1.0912          |
| 1.0418        | 1.0127 | 60   | 1.0428          |
| 1.0105        | 1.5042 | 90   | 1.0232          |
| 1.0082        | 2.0105 | 120  | 1.0127          |
| 0.9946        | 2.5042 | 150  | 1.0074          |
| 0.9826        | 3.0105 | 180  | 1.0057          |
| 0.9898        | 3.5021 | 210  | 1.0055          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1