End of training

README.md

@@ -1,18 +1,13 @@
 ---
-base_model: ybelkada/falcon-7b-sharded-bf16
+library_name: peft
 tags:
+- trl
+- sft
 - generated_from_trainer
+base_model: ybelkada/falcon-7b-sharded-bf16
 model-index:
 - name: falcon-7b-sharded-bf16-finetuned-mental-health-conversational
   results: []
-license: mit
-datasets:
-- heliosbrahma/mental_health_chatbot_dataset
-language:
-- en
-metrics:
-- rouge
-pipeline_tag: conversational
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -20,24 +15,22 @@ should probably proofread and complete it, then remove this comment. -->
 
 # falcon-7b-sharded-bf16-finetuned-mental-health-conversational
 
-This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on a custom [heliosbrahma/mental_health_chatbot_dataset](https://huggingface.co/datasets/heliosbrahma/mental_health_chatbot_dataset) dataset.
+This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
 
 ## Model description
 
-This model is fine-tuned on custom mental health conversational dataset. The rationale behind this is to answer mental health related queries that can be factually verified without responding gibberish words.
+More information needed
 
 ## Intended uses & limitations
 
-The model was trained on the dataset which may contain sensitive information related to mental health. It is important to note that while mental health chatbots built using this model can be helpful, they are not a replacement for professional mental health care.
+More information needed
 
 ## Training and evaluation data
 
-This model was trained on custom [heliosbrahma/mental_health_chatbot_dataset](https://huggingface.co/datasets/heliosbrahma/mental_health_chatbot_dataset) dataset which 172 rows of conversational pair of questions and answers.
+More information needed
 
 ## Training procedure
 
-This model was trained using QLoRA technique to fine-tune on a custom dataset on free-tier GPU available in Google Colab.
-
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -50,7 +43,7 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.03
-- training_steps: 320
+- training_steps: 20
 
 ### Training results
 
@@ -58,7 +51,8 @@ The following hyperparameters were used during training:
 
 ### Framework versions
 
-- Transformers 4.31.0
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.2
-- Tokenizers 0.13.3
+- PEFT 0.7.2.dev0
+- Transformers 4.37.0.dev0
+- Pytorch 2.1.1+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0