Update README.md

README.md

@@ -11,16 +11,20 @@ library_name: adapter-transformers
 
 ## Model Description
 
-A simple opt350m model  trained on the CodeAlpaca dataset using quantization and Progressive Embedding Fine-Tuning (PEFT). It's designed to understand and generate code-related responses based on the prompts provided.
+An  opt-350m model  trained on the CodeAlpaca  20k dataset using quantization and Progressive Embedding Fine-Tuning (PEFT). 
+The resulting model is  designed to understand and generate code-related responses based on the prompts provided.
+
+[original model car](https://huggingface.co/facebook/opt-350m)
 
 ### Model Architecture
 
 - **Base Model**: `facebook/opt-350m`
-- **Fine-tuning**: Progressive Embedding Fine-Tuning (PEFT)
+- **Fine-tuning**: Parameter-Efficient Fine-Tuning (PEFT)
 
 ## Training Data
 
 The model was trained on the `lucasmccabe-lmi/CodeAlpaca-20k` dataset. This dataset contains code-related prompts and their corresponding outputs.
+Script used for training is avaiable [here](https://github.com/harpomaxx/llm-finetuning/blob/0954a7ca16bb25bdef6ee9dd1089867bd4d8e0a5/code/python/scripts/stf_train_opt350m.py)
 
 ## Training Procedure
 
@@ -61,7 +65,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("facebook/opt350m")
 model = AutoModelForCausalLM.from_pretrained("harpomaxx/opt350m-codealpaca20k)
 
-prompt = "### Question: [Your code-related question here]"
+prompt = "Question: [Your code-related question here] ### Answer: "
 inputs = tokenizer.encode(prompt, return_tensors="pt")
 outputs = model.generate(inputs)
 decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)