Delete README.md

README.md

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----
-library_name: peft
-base_model: AI-Sweden-Models/gpt-sw3-1.3b
-datasets:
-- barbaroo/Faroese_BLARK_small
-- barbaroo/Books_Faroese
-language:
-- fo
-- sv
-- is
-- da
-- 'no'
-- en
----
-licence: [LICENCE](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b/blob/main/LICENSE)
-
-# Model Card for Model ID
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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)
-
-```
-
-## Uses
-
-Language generation tasks, such as translation, summarization, conversational AI, etc.
-
-
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --
-[More Information Needed]
-
-
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-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](https://huggingface.co/datasets/barbaroo/Faroese_BLARK_small)
-Extra training data was taken from a private corpus of Faroese books ( [Faroese Books](https://huggingface.co/datasets/barbaroo/Books_Faroese))
-
-
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-Validation/testing was performed on the test split of the Faroese books corpus ( [Faroese Books](https://huggingface.co/datasets/barbaroo/Books_Faroese))
-
-
-
-## 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