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barbaroo/
gptsw3_lora_fo_1.3b

Name: gptsw3_lora_fo_1.3b
Availability: InStock

peft

barbaroo/Faroese_BLARK_small

barbaroo/Books_Faroese

AI-Sweden-Models/gpt-sw3-1.3b

adapter

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licence: LICENCE

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Model Details

Model Description

Developed by: Barbara Scalvini, Language Technology Center, University of the Faroe Islands
Model type: This is a LoRA adapter for GPT-Sw3 with continued pre-training on Faroese data (BLARK corpus, private Faroese books repository). Training was performed for 10 epochs (more checkpoints to come!).
Language(s) (NLP): Swedish, English, Norwegian, Danish, Icelandic, Faroese
from model [optional]: AI-Sweden-Models/gpt-sw3-1.3b

How to Get Started with the Model

Use the code below to get started with the model.

python

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Peft configuration and model
config = PeftConfig.from_pretrained("barbaroo/gptsw3_lora_fo_1.3b")
model = AutoModelForCausalLM.from_pretrained("AI-Sweden-Models/gpt-sw3-1.3b")
model = PeftModel.from_pretrained(model, "barbaroo/gptsw3_lora_fo_1.3b")

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("AI-Sweden-Models/gpt-sw3-1.3b")

# Define the prompt
prompt = "fortel mær eina søgu:"

# Tokenize the input
inputs = tokenizer(prompt, return_tensors="pt")

# Generate text
output = model.generate(**inputs, max_length=100,do_sample=True, temperature=0.7)

# Decode the generated text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(generated_text)

Training Details

Training Data

We trained our model on a corpus derived from the Basic Language Resource Kit for Faroese. For detailed information about the dataset, please see the BLARK_small Extra training data was taken from a private corpus of Faroese books ( Faroese Books)

Testing Data, Factors & Metrics

Testing Data

Validation/testing was performed on the test split of the Faroese books corpus ( Faroese Books)

Training procedure

The following bitsandbytes quantization config was used during training:

quant_method: bitsandbytes
load_in_8bit: True
load_in_4bit: False
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False
llm_int8_has_fp16_weight: False
bnb_4bit_quant_type: fp4
bnb_4bit_use_double_quant: False
bnb_4bit_compute_dtype: float32

Framework versions

PEFT 0.6.2.dev0