|
|
--- |
|
|
license: other |
|
|
library_name: peft |
|
|
tags: |
|
|
- axolotl |
|
|
- generated_from_trainer |
|
|
base_model: NousResearch/Meta-Llama-3-8B |
|
|
model-index: |
|
|
- name: llama-3-8B-semeval2014 |
|
|
results: [] |
|
|
language: |
|
|
- en |
|
|
metrics: |
|
|
- f1 |
|
|
--- |
|
|
|
|
|
<!-- 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: 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|> |
|
|
|
|
|
``` |
|
|
|
|
|
</details><br> |
|
|
|
|
|
# llama-3-8B-semeval2014 |
|
|
|
|
|
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/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](https://simmering.dev/open-absa) |
|
|
|
|
|
## 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 |
