Update README.md

README.md

@@ -13,18 +13,13 @@ license: apache-2.0
 This model is a fine-tuned version of the `CohereForAI/aya-23-8B` base model. It has been fine-tuned using a private dataset of prompt-response pairs that has been curated over the past two years. The fine-tuning process aimed to improve the model's ability to generate relevant and accurate responses in various conversational contexts.
 
 - **Developed by:** Franck Stéphane NDZOMGA
-- **Funded by [optional]:** [More Information Needed]
+- **Funded by [optional]:** FS NDZOMGA
 - **Shared by [optional]:** Franck Stéphane NDZOMGA
 - **Model type:** Causal Language Model with LoRA Adapters
-- **Language(s) (NLP):** Primarily English (add other languages if applicable)
+- **Language(s) (NLP):** Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese
 - **License:** Apache-2.0
 - **Finetuned from model:** CohereForAI/aya-23-8B
 
-### Model Sources [optional]
-
-- **Repository:** [Include the repository link here if publicly available]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
@@ -87,7 +82,7 @@ The model was fine-tuned using a private dataset of prompt-response pairs curate
 #### Training Hyperparameters
 
 - **Precision:** Mixed precision (fp16)
-- **Number of epochs:** [Specify the number of epochs]
+- **Number of epochs:** 1
 - **Batch size:** 1 (gradient accumulation steps: 16 to handle memory issues)
 - **Learning rate:** 5e-5
 - **Warmup steps:** 100
@@ -99,13 +94,6 @@ The model was fine-tuned using a private dataset of prompt-response pairs curate
 - **Remove unused columns:** False
 - **Mixed Precision:** Disabled (fp16=False) to avoid conflicts
 
-### Speeds, Sizes, Times [optional]
-
-- **Training started:** [Date]
-- **Training completed:** [Date]
-- **Average training speed:** [Specify if available]
-- **Model size:** [Specify if available]
-
 ### Additional Information from Training Code
 
 - The training utilized the PEFT (Parameter Efficient Fine-Tuning) library, specifically leveraging the LoRA (Low-Rank Adaptation) method to fine-tune the `CohereForAI/aya-23-8B` model.