Update README.md

README.md

@@ -1,36 +1,61 @@
 ---
-license: apache-2.0
+license: cc-by-nc-4.0
+inference: false
+datasets:
+- BramVanroy/alpaca-cleaned-dutch
 base_model: DAMO-NLP-MT/polylm-1.7b
 tags:
 - generated_from_trainer
+- alpaca
+- Transformers
+- PolyLM
+- text-generation-inference
 model-index:
 - name: polylm_1.7b_ft_alpaca_clean_dutch
   results: []
+language:
+- nl
+library_name: peft
+pipeline_tag: text-generation
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # polylm_1.7b_ft_alpaca_clean_dutch
 
-This model is a fine-tuned version of [DAMO-NLP-MT/polylm-1.7b](https://huggingface.co/DAMO-NLP-MT/polylm-1.7b) on an unknown dataset.
+This adapter model is a fine-tuned version of [DAMO-NLP-MT/polylm-1.7b](https://huggingface.co/DAMO-NLP-MT/polylm-1.7b).
 It achieves the following results on the evaluation set:
 - Loss: 1.8483
 
+Finetuning was performed on the Dutch [BramVanroy/alpaca-cleaned-dutch](https://www.huggingface.co/datasets/BramVanroy/alpaca-cleaned-dutch) dataset which contains 52K of records with instruction following-data translated from English to Dutch.
+
+See [DAMO-NLP-MT/polylm-1.7b](https://huggingface.co/DAMO-NLP-MT/polylm-1.7b) for all information about the base model.
+
 ## Model description
 
 More information needed
 
 ## Intended uses & limitations
 
-More information needed
+The PolyLM-1.7B model was trained on 18 languages. The primary focus was to create a multi-lingual Open LLM.
+Dutch was one of those 18 languages. For training the model a diverse combination of multi-lingual datasets was used. 
+
+The generated output and performance of this model for the Dutch language is very likely not always comparable to the various Open-Llama models that have been finetuned on English Alpaca datasets.
+
+The primary intention of this finetuned model is to explore and research the use of the Dutch language in combination with an Open LLM model.
 
 ## Training and evaluation data
 
-More information needed
+This model was trained on the [BramVanroy/alpaca-cleaned-dutch](https://www.huggingface.co/datasets/BramVanroy/alpaca-cleaned-dutch) dataset.
+
+The dataset is the Dutch translation of the English Alpaca Cleaned instruction dataset.
+
+Based on the dataset license only Non-Commercial use is allowed. Commercial use is strictly forbidden.
 
 ## Training procedure
 
+This model was finetuned with a QLoRA setup on a Google Colab A100 GPU in about 1.5 hours.
+
+The notebook used for training can be found here: [Training Notebook](https://github.com/RobinSmits/Dutch-LLMs/blob/main/PolyLM_1_7B_Alpaca_Clean_Dutch_Qlora.ipynb)
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -45,6 +70,17 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_steps: 64
 - num_epochs: 2
 
+The following bitsandbytes quantization config was used during training:
+- load_in_8bit: False
+- load_in_4bit: True
+- 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: nf4
+- bnb_4bit_use_double_quant: True
+- bnb_4bit_compute_dtype: bfloat16
+
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss |
@@ -69,3 +105,4 @@ The following hyperparameters were used during training:
 - Pytorch 2.0.1+cu118
 - Datasets 2.13.1
 - Tokenizers 0.13.3
+- PEFT 0.4.0