Update README.md

README.md

@@ -1,3 +1,65 @@
 ---
 license: apache-2.0
+datasets:
+- hakurei/open-instruct-v1
+language:
+- en
+tags:
+- code
+- instruction-following
+widget:
+  - text: Tell me how to bake a cake
+    example_title: Baking cakes
+  - text: How can I print a fibonacci series upto N in C++
+    example_title: Coding
 ---
+
+# DialoGPT2 Instruction Following
+
+This is the fine-tuned version of the [microsoft/dialogpt-small](https://huggingface.co/microsoft/DialoGPT-small) on the instruction following task. The dataset used was the [hakurei/open-instruct-v1](https://huggingface.co/datasets/hakurei/open-instruct-v1) dataset.
+
+## Using the model
+
+
+### Using `model.generate()`
+
+To use the model, first call the checkpoints and initialize the model
+
+```python
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("smji/dialogpt2-instruct-following")
+model = AutoModelForCausalLM.from_pretrained("smji/dialogpt2-instruct-following")
+```
+
+And then move onto generating the text
+
+```python
+def generate_text(prompt):
+    inputs = tokenizer.encode(prompt, return_tensors='pt').to(device)
+    outputs = model.generate(inputs, max_length=512, pad_token_id=tokenizer.eos_token_id)
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return generated_text[:generated_text.rfind('.')+1]
+
+generate_text("How can I bake a cake?")
+```
+
+### Using the pipeline
+
+Or, you can also use the pipeline
+
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="smji/dialogpt2-instruct-following")
+
+pipe("How can I bake a cake?", max_length=512)
+```
+
+---
+
+Done by [S M Jishanul Islam](https://github.com/S-M-J-I)
+