Update README.md

README.md

@@ -6,9 +6,47 @@ tags:
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+Base Model: unsloth/DeepSeek-R1-Distill-Llama-8B
+Fine-Tuning Dataset: hieulum/bacsiAI (first 200 rows)
+LoRA Parameters: r=16, lora_alpha=16, target modules, etc.
+Training Details: 1 epoch, 60 steps, batch size 8, learning rate 2e-4
+Usage Instructions:
+  from unsloth import FastLanguageModel
+  model, tokenizer = FastLanguageModel.from_pretrained(
+      model_name="unsloth/DeepSeek-R1-Distill-Llama-8B",
+      max_seq_length=1024,
+      load_in_4bit=True,
+  )
+  model = FastLanguageModel.load_peft_model(model, "your_username/DeepSeek-R1-LoRA-medical")
+
+Programmatically Create a Model Card: Use the huggingface_hub library to generate a basic model card:
+  from huggingface_hub import ModelCard, ModelCardData
+  
+  card_data = ModelCardData(
+      language="en",
+      license="apache-2.0",
+      tags=["medical", "LoRA", "unsloth", "DeepSeek-R1"],
+      datasets=["hieulum/bacsiAI"],
+  )
+  card = ModelCard.from_template(
+      card_data,
+      model_name="DeepSeek-R1-LoRA-medical",
+      model_description="LoRA adapters for medical Q&A, fine-tuned on hieulum/bacsiAI dataset using unsloth.",
+      base_model="unsloth/DeepSeek-R1-Distill-Llama-8B",
+      training_details="Fine-tuned with r=16, lora_alpha=16, 1 epoch, 60 steps, batch size 8, learning rate 2e-4.",
+      usage="Load with unsloth: `FastLanguageModel.load_peft_model(model, 'your_username/DeepSeek-R1-LoRA-medical')`",
+)
+
+# Save the model card locally
+card.save("model_card.md")
+
+# Upload the model card
+from huggingface_hub import upload_file
+upload_file(
+    path_or_fileobj="model_card.md",
+    path_in_repo="README.md",
+    repo_id=repo_id,
+)
 
 ## Model Details