README.md

metadata

license: apache-2.0
datasets:
  - Ericu950/Papyri_1
base_model:
  - meta-llama/Meta-Llama-3.1-8B-Instruct
library_name: transformers
tags:
  - papyrology
  - epigraphy
  - philology

Papy_1_Llama-3.1-8B-Instruct_date

This is a fine-tuned version of the Llama-3.1-8B-Instruct model, specialized in assigning a date to Greek documentary papyri. On a test set of 1,856 unseen papyri, its predictions were, on average, 21.7 years away from the actual date spans.

Dataset

This model is fine-tuned on the Ericu950/Papyri_1 dataset, which consists of Greek documentary papyri texts and their corresponding dates sourced from the amazing Papyri.info.

Usage

To run the model on a GPU with larger memory, following these steps:

1. Download and load the model

import json
from transformers import pipeline, AutoTokenizer, LlamaForCausalLM
import torch

model_id = "Ericu950/Papy_1_Llama-3.1-8B-Instruct_date"

model = LlamaForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
)

tokenizer = AutoTokenizer.from_pretrained(model_id)

generation_pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device_map="auto",
)

2. Run inference on a papyrus fragment of your choice

# This is a rough transcription of Pap.Ups. 106
papyrus_edition = """
ετουσ τεταρτου αυτοκρατοροσ καισαροσ ουεσπασιανου σεβαστου ------------------ 
ομολογει παυσιριων απολλωνιου του παuσιριωνοσ μητροσ ---------------τωι γεγονοτι αυτωι 
εκ τησ γενομενησ και μετηλλαχυιασ αυτου γυναικοσ ------------------------- 
απο τησ αυτησ πολεωσ εν αγυιαι συγχωρειν ειναι ---------------------------------- 
--------------------σ αυτωι εξ ησ συνεστιν ------------------------------------ 
----τησ αυτησ γενεασ την υπαρχουσαν αυτωι οικιαν ------------ 
------------------ ---------καὶ αιθριον και αυλη απερ ο υιοσ διοκοροσ -------------------------- 
--------εγραψεν του δ αυτου διοσκορου ειναι ------------------------------------ 
---------- και προ κατενγεγυηται τα δικαια -------------------------------------- 
νησ κατα τουσ τησ χωρασ νομουσ· εαν δε μη --------------------------------------- 
υπ αυτου τηι του διοσκορου σημαινομενηι -----------------------------------ενοικισμωι του 
ημισουσ μερουσ τησ προκειμενησ οικιασ --------------------------------- διοσκοροσ την τουτων αποχην 
---------------------------------------------μηδ υπεναντιον τουτοισ επιτελειν μηδε 
------------------------------------------------ ανασκευηι κατ αυτησ τιθεσθαι ομολογιαν μηδε 
----------------------------------- επιτελεσαι η χωρισ του κυρια ειναι τα διομολογημενα 
παραβαινειν, εκτεινειν δε τον παραβησομενον τωι υιωι διοσκορωι η τοισ παρ αυτου καθ εκαστην 
εφοδον το τε βλαβοσ και επιτιμον αργυριου δραχμασ 0 και εισ το δημοσιον τασ ισασ και μηθεν 
ησσον· δ -----ιων ομολογιαν συνεχωρησεν·
"""

system_prompt = "Date this papyrus fragment to an exact year!"

input_messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": papyrus_edition},
]

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = generation_pipeline(
    input_messages,
    max_new_tokens=4,
    num_beams=45, # Set this as high as your memory will allow!
    num_return_sequences=1,
    early_stopping=True,
)

beam_contents = []
for output in outputs:
    generated_text = output.get('generated_text', [])
    for item in generated_text:
        if item.get('role') == 'assistant':
            beam_contents.append(item.get('content'))

real_response = "71 or 72 AD"

print(f"Year: {real_response}")
for i, content in enumerate(beam_contents, start=1):
    print(f"Suggestion {i}: {content}")

Expected Output:

Year: 71 or 72 AD
Suggestion 1: 71

Usage on free tier in Google Colab

If you don’t have access to a larger GPU but want to try the model out, you can run it in a quantized format in Google Colab. The quality of the responses might deteriorate significantly. Follow these steps:

Step 1: Install Dependencies

!pip install -U bitsandbytes
import os
os._exit(00)

Step 2: Download and quantize the model

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
import torch

quant_config = BitsAndBytesConfig(
   load_in_4bit=True,
   bnb_4bit_quant_type="nf4",
   bnb_4bit_use_double_quant=True,
   bnb_4bit_compute_dtype=torch.bfloat16
)

model = AutoModelForCausalLM.from_pretrained("Ericu950/Papy_1_Llama-3.1-8B-Instruct_date",
device_map = "auto", quantization_config = quant_config)
tokenizer = AutoTokenizer.from_pretrained("Ericu950/Papy_1_Llama-3.1-8B-Instruct_date")

generation_pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device_map="auto",
)

Step 3: Run inference on a papyrus fragment of your choice

# This is a rough transcription of Pap.Ups. 106
papyrus_edition = """
ετουσ τεταρτου αυτοκρατοροσ καισαροσ ουεσπασιανου σεβαστου ------------------ 
ομολογει παυσιριων απολλωνιου του παuσιριωνοσ μητροσ ---------------τωι γεγονοτι αυτωι 
εκ τησ γενομενησ και μετηλλαχυιασ αυτου γυναικοσ ------------------------- 
απο τησ αυτησ πολεωσ εν αγυιαι συγχωρειν ειναι ---------------------------------- 
--------------------σ αυτωι εξ ησ συνεστιν ------------------------------------ 
----τησ αυτησ γενεασ την υπαρχουσαν αυτωι οικιαν ------------ 
------------------ ---------καὶ αιθριον και αυλη απερ ο υιοσ διοκοροσ -------------------------- 
--------εγραψεν του δ αυτου διοσκορου ειναι ------------------------------------ 
---------- και προ κατενγεγυηται τα δικαια -------------------------------------- 
νησ κατα τουσ τησ χωρασ νομουσ· εαν δε μη --------------------------------------- 
υπ αυτου τηι του διοσκορου σημαινομενηι -----------------------------------ενοικισμωι του 
ημισουσ μερουσ τησ προκειμενησ οικιασ --------------------------------- διοσκοροσ την τουτων αποχην 
---------------------------------------------μηδ υπεναντιον τουτοισ επιτελειν μηδε 
------------------------------------------------ ανασκευηι κατ αυτησ τιθεσθαι ομολογιαν μηδε 
----------------------------------- επιτελεσαι η χωρισ του κυρια ειναι τα διομολογημενα 
παραβαινειν, εκτεινειν δε τον παραβησομενον τωι υιωι διοσκορωι η τοισ παρ αυτου καθ εκαστην 
εφοδον το τε βλαβοσ και επιτιμον αργυριου δραχμασ 0 και εισ το δημοσιον τασ ισασ και μηθεν 
ησσον· δ -----ιων ομολογιαν συνεχωρησεν·"""

system_prompt = "Date this papyrus fragment to an exact year!"

input_messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": papyrus_edition},
]

outputs = generation_pipeline(
    input_messages,
    max_new_tokens=4,
    num_beams=10,
    num_return_sequences=1,
    early_stopping=True,
)

beam_contents = []
for output in outputs:
    generated_text = output.get('generated_text', [])
    for item in generated_text:
        if item.get('role') == 'assistant':
            beam_contents.append(item.get('content'))

real_response = "71 or 72 AD"

print(f"Year: {real_response}")
for i, content in enumerate(beam_contents, start=1):
    print(f"Suggestion {i}: {content}")

Expected Output:

Year: 71 or 72 AD
Suggestion 1: 71