Update README.md

README.md

@@ -1,49 +1,53 @@
----
-license: mit
-datasets:
-  - openwebtext
-  - alpacha
-tags:
-  - text-generation
-  - gpt-2
-  - openwebtext
-  - alpacha
-model_name: gpt2-124M
-language: en
----
-
-# GPT-2 124M Fine-tuned on OpenWebText and Alpacha
-
-This model is a fine-tuned version of GPT-2 (124M parameters) trained on the OpenWebText dataset and further fine-tuned on the Alpacha dataset.
-
-## Model Description
-
-This model is based on the GPT-2 architecture and has been fine-tuned on a combination of two datasets:
-
-1. **OpenWebText**: The model was initially trained on the OpenWebText dataset for 600,000 iterations.
-2. **Alpacha**: The model was further fine-tuned on the Alpacha dataset for the remaining 50,000 iterations.
-
-The model was trained using a **laptop with an RTX 3060 GPU** for a total of **650,000 iterations** (approximately **8 days** of training).
-
-## Hardware Details
-
-- **GPU**: Laptop with an **RTX 3060**
-- **Training Time**: The model took **8 days** (approximately 650,000 iterations) to train.
-- **Total Iterations**: 650,000 iterations (600,000 on OpenWebText + 50,000 on Alpacha).
-
-## How to Use
-
-You can use this model for text generation with the Hugging Face `transformers` library:
-
-```python
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-
-model = GPT2LMHeadModel.from_pretrained("Aaltjo/gpt2-124M-openwebtext-alpacha")
-tokenizer = GPT2Tokenizer.from_pretrained("Aaltjo/gpt2-124M-openwebtext-alpacha")
-
-input_text = "Once upon a time"
-inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model.generate(**inputs)
-
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
+---
+license: mit
+datasets:
+  - openwebtext
+  - alpacha
+tags:
+  - text-generation
+  - gpt-2
+  - openwebtext
+  - alpacha
+model_name: gpt2-124M
+language: en
+---
+
+# GPT-2 124M Fine-tuned on OpenWebText and Alpacha
+
+This model is a fine-tuned version of GPT-2 (124M parameters) trained on the OpenWebText dataset and further fine-tuned on the Alpacha dataset.
+
+## Model Description
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d46028974775cbcb214e42/Kgv0Z-hx0opfTdyE2khux.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d46028974775cbcb214e42/poPiR0yN0Ar_hqVGliNKE.png)
+
+This model is based on the GPT-2 architecture and has been fine-tuned on a combination of two datasets:
+
+1. **OpenWebText**: The model was initially trained on the OpenWebText dataset for 600,000 iterations.
+2. **Alpacha**: The model was further fine-tuned on the Alpacha dataset for the remaining 50,000 iterations.
+
+The model was trained using a **laptop with an RTX 3060 GPU** for a total of **650,000 iterations** (approximately **8 days** of training).
+
+## Hardware Details
+
+- **GPU**: Laptop with an **RTX 3060**
+- **Training Time**: The model took **8 days** (approximately 650,000 iterations) to train.
+- **Total Iterations**: 650,000 iterations (600,000 on OpenWebText + 50,000 on Alpacha).
+
+## How to Use
+
+You can use this model for text generation with the Hugging Face `transformers` library:
+
+```python
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+model = GPT2LMHeadModel.from_pretrained("Aaltjo/gpt2-124M-openwebtext-alpacha")
+tokenizer = GPT2Tokenizer.from_pretrained("Aaltjo/gpt2-124M-openwebtext-alpacha")
+
+input_text = "Once upon a time"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```