llama3-conciser

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<details><summary>See axolotl config</summary>

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

</details><br>

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

Framework versions

• PEFT 0.10.0
• Transformers 4.40.2
• Pytorch 2.2.2+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1