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psimm/
llama-3-8B-semeval2014

Name: llama-3-8B-semeval2014
Availability: InStock

generated_from_trainer

NousResearch/Meta-Llama-3-8B

adapter

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See axolotl config

axolotl version: 0.4.0

yaml

base_model: NousResearch/Meta-Llama-3-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: semeval2014_train.jsonl
    ds_type: json
    type:
      # JSONL file contains instruction, input, output fields per line.
      # This gets mapped to the equivalent axolotl tags.
      field_instruction: instruction
      field_input: input
      field_output: output
      # Format is used by axolotl to generate the prompt.
      format: |-
        [INST] {input} [/INST]

tokens: # add new control tokens from the dataset to the model
  - "[INST]"
  - "[/INST]"

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lora-out

sequence_len: 4096
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false

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

wandb_project: absa-semeval2014
wandb_entity: psimm
wandb_log_model:
wandb_name: llama-3-8B-semeval2014

hub_model_id: psimm/llama-3-8B-semeval2014

gradient_accumulation_steps: 1
micro_batch_size: 32
num_epochs: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0001

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

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

warmup_steps: 10
eval_steps: 0.05
eval_table_size:
eval_table_max_new_tokens: 128
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

llama-3-8B-semeval2014

This model is a fine-tuned version of NousResearch/Meta-Llama-3-8B on the SemEval2014 Task 4 dataset. It achieves the following results on the evaluation set:

Loss: 0.0695
F1 Score: 82.13

For more details, see my article

Intended uses & limitations

Aspect-based sentiment analysis in English. Pass it review sentences wrapped in tags, like this: [INST]The cheeseburger was tasty but the fries were soggy.[/INST]

How to run

This adapter requires that two new tokens are added to the tokenizer. The tokens are: "[INST]" and "[/INST]". Also, the base model's embedding layer size has to be increased by 2.

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

extra_tokens = ["[INST]", "[/INST]"]
base_model = "NousResearch/Meta-Llama-3-8B"

base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Meta-Llama-3-8B")
base_model.resize_token_embeddings(base_model.config.vocab_size + len(extra_tokens))

tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B")

tokenizer.add_special_tokens({"additional_special_tokens": extra_tokens})

model = PeftModel.from_pretrained(base_model, "psimm/llama-3-8B-semeval2014")

input_text = "[INST]The food was tasty[/INST]"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

gen_tokens = model.generate(
    input_ids,
    max_length=256,
    temperature=0.01,
)

# Remove the input tokens
output_tokens = gen_tokens[:, input_ids.shape[1] :]

print(tokenizer.batch_decode(output_tokens, skip_special_tokens=True))

Training and evaluation data

SemEval 2014 Task 4 reviews.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 64
total_eval_batch_size: 64
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
2.5408	0.0112	1	2.2742
0.1159	0.2022	18	0.1026
0.1028	0.4045	36	0.0762
0.0813	0.6067	54	0.0709
0.0908	0.8090	72	0.0665
0.0431	1.0112	90	0.0639
0.0275	1.2135	108	0.0663
0.0224	1.4157	126	0.0659
0.0349	1.6180	144	0.0637
0.0281	1.8202	162	0.0589
0.0125	2.0225	180	0.0592
0.0088	2.2247	198	0.0682
0.0076	2.4270	216	0.0666
0.01	2.6292	234	0.0654
0.0131	2.8315	252	0.0704
0.0075	3.0337	270	0.0679
0.002	3.2360	288	0.0688
0.0029	3.4382	306	0.0692
0.0009	3.6404	324	0.0694
0.0064	3.8427	342	0.0695

Framework versions

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