See axolotl config

axolotl version: 0.4.0

###
# 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 on my 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