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--- |
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library_name: peft |
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base_model: AI-Sweden-Models/gpt-sw3-1.3b |
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datasets: |
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- barbaroo/Faroese_BLARK_small |
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- barbaroo/Books_Faroese |
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language: |
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- fo |
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- sv |
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- is |
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- da |
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- 'no' |
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- en |
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--- |
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licence: [LICENCE](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b/blob/main/LICENSE) |
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# Model Card for Model ID |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Barbara Scalvini, Language Technology Center, University of the Faroe Islands |
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- **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!). |
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- **Language(s) (NLP):** Swedish, English, Norwegian, Danish, Icelandic, Faroese |
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- **from model [optional]:** AI-Sweden-Models/gpt-sw3-1.3b |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load the Peft configuration and model |
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config = PeftConfig.from_pretrained("barbaroo/gptsw3_lora_fo_1.3b") |
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model = AutoModelForCausalLM.from_pretrained("AI-Sweden-Models/gpt-sw3-1.3b") |
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model = PeftModel.from_pretrained(model, "barbaroo/gptsw3_lora_fo_1.3b") |
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# Load the tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("AI-Sweden-Models/gpt-sw3-1.3b") |
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# Define the prompt |
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prompt = "fortel mær eina søgu:" |
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# Tokenize the input |
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inputs = tokenizer(prompt, return_tensors="pt") |
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# Generate text |
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output = model.generate(**inputs, max_length=100,do_sample=True, temperature=0.7) |
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# Decode the generated text |
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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print(generated_text) |
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``` |
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## Training Details |
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### Training Data |
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<!-- 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. -- |
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[More Information Needed] |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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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) |
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Extra training data was taken from a private corpus of Faroese books ( [Faroese Books](https://huggingface.co/datasets/barbaroo/Books_Faroese)) |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Data Card if possible. --> |
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Validation/testing was performed on the test split of the Faroese books corpus ( [Faroese Books](https://huggingface.co/datasets/barbaroo/Books_Faroese)) |
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## Training procedure |
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The following `bitsandbytes` quantization config was used during training: |
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- quant_method: bitsandbytes |
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- load_in_8bit: True |
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- load_in_4bit: False |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: fp4 |
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- bnb_4bit_use_double_quant: False |
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- bnb_4bit_compute_dtype: float32 |
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### Framework versions |
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- PEFT 0.6.2.dev0 |
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