barbaroo
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gptsw3_lora_fo_1.3b

PEFT

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barbaroo commited on Jul 1

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 ---
 library_name: peft
 base_model: AI-Sweden-Models/gpt-sw3-1.3b
 ---
 # Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
@@ -17,186 +25,69 @@ base_model: AI-Sweden-Models/gpt-sw3-1.3b
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
 ### Training Data
-<!-- This should link to a Dataset 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]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
 ### Testing Data, Factors & Metrics
 #### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
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-#### Hardware
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-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
-[More Information Needed]
 ## Training procedure

 ---
 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
+- **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
+<!-- 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