barbaroo/gptsw3_lora_fo_40b

Model Card for Model ID

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 4 epochs.

• Language(s) (NLP): Swedish, English, Norwegian, Danish, Icelandic, Faroese

• from model [optional]: AI-Sweden-Models/gpt-sw3-40b

How to Get Started with the Model

Use the code below to get started with the model.

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM

config = PeftConfig.from_pretrained("barbaroo/gptsw3_lora_fo_40b")
model = AutoModelForCausalLM.from_pretrained("AI-Sweden-Models/gpt-sw3-40b")
model = PeftModel.from_pretrained(model, "barbaroo/gptsw3_lora_fo_40b")

[More Information Needed]

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