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---
base_model: meta-llama/Meta-Llama-3-8B
library_name: peft
license: llama3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: llama-3-8b-ocr-correction
results: []
---
<!-- 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: `0.4.1`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: true
strict: false
lora_fan_in_fan_out: false
data_seed: 49
seed: 49
datasets:
- path: ft_data/alpaca_data.jsonl
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./qlora-alpaca-out
hub_model_id: pbevan11/llama-3-8b-ocr-correction
adapter: qlora
lora_model_dir:
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project: ocr-ft
wandb_entity: sncds
wandb_name: test
gradient_accumulation_steps: 4
micro_batch_size: 2 # was 16
eval_batch_size: 2 # was 16
num_epochs: 2
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: "<|end_of_text|>"
```
</details><br>
# llama-3-8b-ocr-correction
This model is a qlora fine-tuned adapter for [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the [pbevan11/synthetic-ocr-correction-gpt4o](https://huggingface.co/datasets/pbevan11/synthetic-ocr-correction-gpt4o) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1742
## Usage
First, download the model
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model_id='pbevan11/llama-3-8b-ocr-correction'
model = AutoPeftModelForCausalLM.from_pretrained(model_id).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
```
Then, construct the prompt template like so:
```python
def prompt(instruction, inp):
return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{inp}
### Response:
"""
def prompt_tok(instruction, inp, return_ids=False):
_p = prompt(instruction, inp)
input_ids = tokenizer(_p, return_tensors="pt", truncation=True).input_ids.cuda()
out_ids = model.generate(input_ids=input_ids, max_new_tokens=5000,
do_sample=False)
ids = out_ids.detach().cpu().numpy()
if return_ids: return out_ids
full_output = tokenizer.batch_decode(ids, skip_special_tokens=True)[0]
response_start = full_output.find("### Response:")
if response_start != -1:
return full_output[response_start + len("### Response:"):]
else:
return full_output[len(_p):]
```
Finally, you can get predictions like this:
```python
# model inputs
instruction = "You are an assistant that takes a piece of text that has been corrupted during OCR digitisation, and produce a corrected version of the same text."
inp = "Do Not Kule Oi't hy.er-l'rieed AjijqIi: imac - Analyst (fteuiers) Hcuiers - A | ) | ilf, <;/) in |) nter |iic . conic! deeiilf. l.o sell n lower-|)rieofl wersinn oi its Macintosh cornutor to nttinct ronsnnu-rs already euami'red ot its iPod music jiayo-r untl annoyoil. by sccnrit.y problems ivitJi Willtlows PCs , Piper.iaffray analyst. (Jcne Muster <aid on Tlinrtiday."
# print prediction
out = prompt_tok(instruction, inp)
print(out.replace('\\', ' '))
```
This will give you a prediction that looks like this:
```md
"Do Not Rule Out Lower-Priced Mac - Analyst (Reuters) Reuters - Apple Inc. may be considering a lower-priced version of its Macintosh computer to attract consumers already enamored of its iPod music player and annoyed by security problems with Windows PCs, PiperJaffray analyst Gene Munster said on Thursday."
```
Alternatively, you can play with this model on Replicate: [tbc](tbc)
## Intended uses & limitations
Reconstructions should not be taken as the truth, the model is likely to make some things up to fill in the gaps, and so some things may not be perfectly histoically acurate.
This model was intended to be used to restore historical documents that have been imperfectly digitalised using OCR.
## Training and evaluation data
TBC: evaluating on the test set from [pbevan11/synthetic-ocr-correction-gpt4o](https://huggingface.co/pbevan11/synthetic-ocr-correction-gpt4o)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 49
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6611 | 0.0165 | 1 | 0.6229 |
| 0.3149 | 0.2469 | 15 | 0.2870 |
| 0.2074 | 0.4938 | 30 | 0.2166 |
| 0.2211 | 0.7407 | 45 | 0.1937 |
| 0.195 | 0.9877 | 60 | 0.1825 |
| 0.1411 | 1.2140 | 75 | 0.1787 |
| 0.1348 | 1.4609 | 90 | 0.1760 |
| 0.1479 | 1.7078 | 105 | 0.1743 |
| 0.1413 | 1.9547 | 120 | 0.1742 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1 |