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mjobe105
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qlora-dolphindataset

PEFT
Safetensors
llama
Generated from Trainer
4-bit precision
bitsandbytes
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qlora-dolphindataset
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---
license: llama3
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-70B-Instruct
model-index:
- name: workspace/data/out/qlora
  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: meta-llama/Meta-Llama-3-70B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: /workspace/data/dataset/hex_phi_dolphin_responses.jsonl
    ds_type: json
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: /workspace/data/out/qlora

adapter: qlora
lora_model_dir:

sequence_len: 512
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
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: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00001

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

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

warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>


```

</details><br>

# workspace/data/out/qlora

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0876

## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 10
- gradient_accumulation_steps: 4
- total_train_batch_size: 80
- total_eval_batch_size: 20
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.7723        | 0.2667 | 1    | 2.0884          |
| 1.8176        | 0.5333 | 2    | 2.0872          |
| 1.8499        | 0.8    | 3    | 2.0874          |
| 1.7963        | 1.0667 | 4    | 2.0865          |
| 1.8762        | 1.3333 | 5    | 2.0866          |
| 1.7795        | 1.6    | 6    | 2.0875          |
| 1.8179        | 1.8667 | 7    | 2.0880          |
| 1.8353        | 2.1333 | 8    | 2.0874          |
| 1.8009        | 2.4    | 9    | 2.0864          |
| 1.7625        | 2.6667 | 10   | 2.0869          |
| 1.8273        | 2.9333 | 11   | 2.0874          |
| 1.8198        | 3.2    | 12   | 2.0876          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1