Create README.md

README.md

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+---
+datasets:
+- mlabonne/Evol-Instruct-Python-26k
+language:
+- en
+library_name: adapter-transformers
+tags:
+- code
+---
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Zidan Alfarizi
+- **Model type:** large language model for code generation
+- **Language(s) (NLP):** English
+- **Finetuned from model:** llama2
+
+### Model Sources 
+
+- **Repository:** https://github.com/unslothai/unsloth
+- **Developed by:** unsloth
+
+### Model parameter
+
+- r = 16, 
+- target_modules = ["q_proj", "k_proj", "v_proj", "o_proj","gate_proj", "up_proj", "down_proj",],
+- lora_alpha = 16,
+- lora_dropout = 0, 
+- bias = "none",    
+- use_gradient_checkpointing = "unsloth", 
+- random_state = 3407,
+- use_rslora = False,  
+- loftq_config = None, 
+
+## Usage and limitations
+
+This model is used to generate code based on commands given by the user. it should be noted that this model can generate many languages because it takes the initial model from llama2. However, after finetuning it is better at generating python code, because currently it is only trained with python code datasets. 
+
+## How to Get Started with the Model
+
+use link below to use model
+https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing
+
+### Training Data
+
+
+
+https://huggingface.co/datasets/mlabonne/Evol-Instruct-Python-26k
+
+
+#### Training Hyperparameters
+
+- **Warmup_step:** 5
+- **lr_scheduler_type:** linear
+- **Learning Rate:** 0.0002
+- **Batch Size:** 2
+- **Weigh_decay:** 0.001
+- **Epoch:** 30
+- **Optimizer:** adamw_8bit
+
+#### Testing Data
+
+https://huggingface.co/datasets/google-research-datasets/mbpp/viewer/full
+
+### Testing Document
+
+https://docs.google.com/spreadsheets/d/1hr8R4nixQsDC5cGGENTOLUW1jPCS_lltVRIeOzenBvA/edit?usp=sharing
+
+### Results
+
+Berfore finetune 
+- Accurary : 56%
+- Consistensy : 0.4%
+
+After fine tune 
+- Accuracy : 59%
+- Consistency : 100%