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---
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Llama-3-8B
model-index:
- name: qlora_decrease_lr_promptfix
  results: []
license: llama3
datasets:
- muellerzr/llama-3-8b-self-align-data-generation-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. -->

## Llama-3 8B Self-Instruct: PEFT Edition

This model is the result of recreating the [StarCoder2 Self-Instruct](https://huggingface.co/blog/sc2-instruct) pipeline, but applied to Llama-3-8B.

It could not have been done without the blood, sweat, and tears of my dear friends who have helped me along the way with training my first LLM.

A blog will come shortly detailing the many training runs and failures during this.

[<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: llama3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: llama-3-8b-self-align-data-generation-results/sanitized.jsonl
    ds_type: json
    type:
      system_prompt: "You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions."
      field_system: system
      field_instruction: instruction
      field_output: response
      format: "### Instruction:\n{instruction}\n\n### Response:\n"
      no_input_format: "### Instruction:\n{instruction}\n\n### Response:\n"
dataset_prepared_path:
val_set_size: 0.05

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

adapter: qlora
save_safetensors: true
lora_model_dir:
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

log_with: None
wandb_project: llama-3-8b-self-align-axolotl
wandb_entity:
wandb_watch:
wandb_name: qlora-prince-hps-promptfix

output_dir: qlora_decrease_lr_promptfix
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-5

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 8
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 2
debug:
deepspeed:
weight_decay: 0.0
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: false
  fsdp_offload_params: false
  fsdp_use_orig_params: false
  fsdp_cpu_ram_efficient_loading: false
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_sharding_strategy: FULL_SHARD
special_tokens:
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
tokens:
  - "<|im_start|>"
  - "<|im_end|>"
lora_modules_to_save:
  - embed_tokens
  - lm_head
```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/muellerzr/llama-3-8b-self-align-axolotl/runs/2q8jhm3e)
# qlora_decrease_lr_promptfix

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4121

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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: 100
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6903        | 0.0061 | 1    | 0.6706          |
| 0.6463        | 0.1285 | 21   | 0.6392          |
| 0.4944        | 0.2571 | 42   | 0.4806          |
| 0.4495        | 0.3856 | 63   | 0.4532          |
| 0.4444        | 0.5142 | 84   | 0.4406          |
| 0.4185        | 0.6427 | 105  | 0.4334          |
| 0.4336        | 0.7712 | 126  | 0.4286          |
| 0.4061        | 0.8998 | 147  | 0.4252          |
| 0.4002        | 1.0145 | 168  | 0.4221          |
| 0.4013        | 1.1431 | 189  | 0.4205          |
| 0.3674        | 1.2716 | 210  | 0.4189          |
| 0.3942        | 1.4002 | 231  | 0.4175          |
| 0.3984        | 1.5287 | 252  | 0.4165          |
| 0.3867        | 1.6572 | 273  | 0.4150          |
| 0.3872        | 1.7858 | 294  | 0.4137          |
| 0.401         | 1.9143 | 315  | 0.4130          |
| 0.3602        | 2.0275 | 336  | 0.4126          |
| 0.3817        | 2.1561 | 357  | 0.4131          |
| 0.3592        | 2.2846 | 378  | 0.4129          |
| 0.3729        | 2.4132 | 399  | 0.4127          |
| 0.372         | 2.5417 | 420  | 0.4121          |
| 0.3685        | 2.6702 | 441  | 0.4120          |
| 0.3732        | 2.7988 | 462  | 0.4115          |
| 0.38          | 2.9273 | 483  | 0.4112          |
| 0.3637        | 3.0413 | 504  | 0.4114          |
| 0.3628        | 3.1699 | 525  | 0.4118          |
| 0.355         | 3.2984 | 546  | 0.4122          |
| 0.3646        | 3.4269 | 567  | 0.4121          |
| 0.3496        | 3.5555 | 588  | 0.4121          |
| 0.3573        | 3.6840 | 609  | 0.4121          |
| 0.3598        | 3.8125 | 630  | 0.4121          |
| 0.3669        | 3.9411 | 651  | 0.4121          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1