README.md

---
base_model: Qwen/Qwen2-7B
#base_model: /workspace/data/models/Qwen2-7B
library_name: peft
tags:
- generated_from_trainer
model-index:
- name: workspace/data/outputs/Qwen2-7B-TestInstructFinetune-LORA
  results: []
---

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 is my terrible attempt to instruct tune base Qwen2-7B, I haven't even tested this yet, I'll do that eventually...

EDIT: Tested it for a bit, seems to actually work ok, not amazing, but actually not bad, I’ll do another once I learn more about instruct tuning...
<!-- 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-TestInstructFinetune-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: 4
eval_table_size:
saves_per_epoch: 4
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-TestInstructFinetune-LORA

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5037

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.6232        | 0.0027 | 1    | 0.6296          |
| 0.5602        | 0.2499 | 91   | 0.5246          |
| 0.4773        | 0.4998 | 182  | 0.5155          |
| 0.4375        | 0.7497 | 273  | 0.5116          |
| 0.6325        | 0.9997 | 364  | 0.5092          |
| 0.4385        | 1.2382 | 455  | 0.5073          |
| 0.4949        | 1.4882 | 546  | 0.5061          |
| 0.503         | 1.7381 | 637  | 0.5052          |
| 0.5023        | 1.9880 | 728  | 0.5046          |
| 0.3737        | 2.2238 | 819  | 0.5041          |
| 0.505         | 2.4737 | 910  | 0.5039          |
| 0.4833        | 2.7237 | 1001 | 0.5038          |
| 0.4986        | 2.9736 | 1092 | 0.5037          |
| 0.5227        | 3.2108 | 1183 | 0.5037          |
| 0.5723        | 3.4607 | 1274 | 0.5037          |
| 0.4692        | 3.7106 | 1365 | 0.5037          |
| 0.5222        | 3.9605 | 1456 | 0.5037          |


### Framework versions

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