update README.md more details

README.md

@@ -109,10 +109,59 @@ special_tokens:
 
 # deepseek-coder-1.3b-typescript
 
-This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on the the-stack dataset, using 0.5B of tokens of typescript only.
+CodeGPTPlus/deepseek-coder-1.3b-typescript, emerges as a fine-tuned iteration of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base), meticulously crafted by the CodeGPT team to excel in generating expert code in TypeScript. With specific fine-tuning for TypeScript and a dataset of 0.5B tokens, this model excels in producing precise and efficient solutions in this programming language.
+The 16K window size and an additional fill-in-the-middle task are employed to deliver project-level code completion.
+This new model stands as the ideal choice for those seeking a specialized code generator for TypeScript, backed by the expertise of the CodeGPT team. 
+
 It achieves the following results on the evaluation set:
 - Loss: 0.7681
 
+**Model Developers** CodeGPT Team
+**Variations**  1.3B
+**Input** Models input text only.
+**Output** Models generate text only.
+
+## How to Use
+This model is for completion purposes only. Here give some examples of how to use the model.
+
+#### Running the model on a GPU
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("CodeGPTPlus/deepseek-coder-1.3b-typescript",
+                                          trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("CodeGPTPlus/deepseek-coder-1.3b-typescript",
+                                              trust_remote_code=True).cuda()
+
+input_text = """<｜fim▁begin｜>function quickSort(arr: number[]): number[] {
+  if (arr.length <= 1) {
+    return arr;
+  }
+  const pivot = arr[0];
+  const left = [];
+  const right = [];
+<｜fim▁hole｜>
+  return [...quickSort(left), pivot, ...quickSort(right)];
+}<｜fim▁end｜>"""
+
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_length=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Fill In the Middle (FIM)
+```python
+<｜fim▁begin｜>function quickSort(arr: number[]): number[] {
+  if (arr.length <= 1) {
+    return arr;
+  }
+  const pivot = arr[0];
+  const left = [];
+  const right = [];
+<｜fim▁hole｜>
+  return [...quickSort(left), pivot, ...quickSort(right)];
+}<｜fim▁end｜>
+```
+
 ## Training procedure
 
 ### Training hyperparameters