---
license: other
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: NousResearch/Meta-Llama-3-8B
model-index:
- name: llama3-conciser
results: []
pipeline_tag: text2text-generation
datasets:
- chrislee973/llama3-conciser-dataset
---
[
](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config
axolotl version: `0.4.0`
```yaml
###
# Model Configuration: LLaMA-3 8B
###
# Copied from most recent modal llm-finetuning repo
base_model: NousResearch/Meta-Llama-3-8B
sequence_len: 4096
# base model weight quantization
load_in_8bit: true
# attention implementation
flash_attention: true
# finetuned adapter config
adapter: lora
lora_model_dir:
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save: # required when adding new tokens to LLaMA/Mistral
- embed_tokens
- lm_head
# for details, see https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
###
# Dataset Configuration: sqlqa
###
datasets:
# This will be the path used for the data when it is saved to the Volume in the cloud.
- path: conciser_dataset_50.jsonl
ds_type: json
type:
# JSONL file contains question, context, answer fields per line.
# This gets mapped to instruction, input, output axolotl tags.
field_instruction: instruction
field_input: text
field_output: cleaned_text
# Format is used by axolotl to generate the prompt.
format: |-
[INST] {instruction}
{input}
[/INST]
# dataset formatting config
tokens: # add new control tokens from the dataset to the model
- "[INST]"
- " [/INST]"
- "[RES]"
- " [/RES]"
special_tokens:
pad_token: <|end_of_text|>
val_set_size: 0.05
###
# Training Configuration
###
# random seed for better reproducibility
seed: 117
# optimizer config
optimizer: adamw_bnb_8bit
# optimizer: adamw_torch
learning_rate: 0.0001
lr_scheduler: cosine
num_epochs: 4
micro_batch_size: 2
gradient_accumulation_steps: 1
warmup_steps: 10
# axolotl saving config
dataset_prepared_path: last_run_prepared
output_dir: ./lora-out
# logging and eval config
logging_steps: 1
eval_steps: 0.05
# training performance optimization config
bf16: auto
tf32: false
gradient_checkpointing: true
###
# Miscellaneous Configuration
###
# when true, prevents over-writing the config from the CLI
strict: false
# "Don't mess with this, it's here for accelerate and torchrun" -- axolotl docs
local_rank:
# wandb logging config
wandb_project: llama3-conciser
wandb_name: llama3-4epochs-2batchsize-pushtohub
hub_model_id: chrislee973/llama3-conciser
```
# llama3-conciser
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on my [conciser dataset](https://huggingface.co/datasets/chrislee973/llama3-conciser-dataset).
## Uses
### Text Revision task
Given an input of a paragraph of text from a transcript, it lightly touches up and edits the sentences and phrases,
improving the flow and readability of the text while maintaining the speaker's original intention.
For example, given the following input text:
```
I think I sort of deep down believed in what we were doing, and I did some analysis. I was like, okay, well, what would I go do if I wasn't doing this? It's like, well, I really like building things, and I like helping people communicate, and I like understanding what's going on with people and the dynamics between people. So I think if I sold this company, I'd just go build another company like this. And I kind of like the one I have.
```
the revised output text is:
```
I believed deep down in what we were doing. I did some analysis. What would I go do if I wasn’t doing this? I really like building things, helping people communicate, understanding what’s going on with people and the dynamics between them. If I sold this company, I’d just go build another one like this. I kind of like the one I have.
```
There are still some rough edges around the model as a result of my dataset being so tiny (just 50 examples). I hope to smooth these imperfections out and close the quality gap by adding many more examples to the dataset.
## Usage
TODO: add sample inference code
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 117
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 4
- 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8738 | 0.0833 | 1 | 0.7897 |
| 1.2209 | 0.25 | 3 | 0.7878 |
| 0.8204 | 0.5 | 6 | 0.6336 |
| 0.6652 | 0.75 | 9 | 0.5303 |
| 0.4086 | 1.0 | 12 | 0.4836 |
| 0.3365 | 1.25 | 15 | 0.4733 |
| 0.3445 | 1.5 | 18 | 0.5132 |
| 0.3641 | 1.75 | 21 | 0.5146 |
| 0.1941 | 2.0 | 24 | 0.4939 |
| 0.1814 | 2.25 | 27 | 0.4863 |
| 0.1342 | 2.5 | 30 | 0.4969 |
| 0.1978 | 2.75 | 33 | 0.5141 |
| 0.1589 | 3.0 | 36 | 0.5222 |
| 0.1184 | 3.25 | 39 | 0.5258 |
| 0.1513 | 3.5 | 42 | 0.5182 |
| 0.1172 | 3.75 | 45 | 0.5155 |
| 0.0607 | 4.0 | 48 | 0.5174 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.2.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1