Update README.md

README.md

@@ -1,6 +1,11 @@
 ---
 library_name: peft
 base_model: Qwen/Qwen1.5-1.8B-Chat
+datasets:
+- b-mc2/sql-create-context
+language:
+- en
+pipeline_tag: text-generation
 ---
 
 # Qwen-1.5-1.8B-SQL Model
@@ -11,7 +16,7 @@ This model, `deltawi/Qwen-1.5-1.8B-SQL`, is fine-tuned on SQL generation based o
 ## Installation
 To use this model, you need to install the `transformers` library from Hugging Face. You can do this using pip:
 ```bash
-pip install transformers
+pip install transformers huggingface_hub accelerate peft
 ```
 
 ## Usage
@@ -20,16 +25,26 @@ pip install transformers
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 # Set the device
-device = "mps"  # replace with your device: "cpu", "cuda", "mps"
+device = "cuda"  # replace with your device: "cpu", "cuda", "mps"
 
-# Load the model
-model = AutoModelForCausalLM.from_pretrained(
-    "deltawi/Qwen-1.5-1.8B-SQL",
-    device_map="auto"
-)
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import random 
+
+
+peft_model_id = "deltawi/Qwen-1.5-1.8B-SQL"
+base_model_id = "Qwen/Qwen1.5-1.8B-Chat"
+
+device = "cuda"
 
-# Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B-Chat")
+model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")
+model.load_adapter(peft_model_id)
+tokenizer = AutoTokenizer.from_pretrained(
+        "deltawi/Qwen-1.5-1.8B-SQL",
+        #model_max_length=2048,
+        padding_side="right",
+        trust_remote_code=True,
+        pad_token='<|endoftext|>'
+    )
 
 # Define your question and context
 Question = "Your question here"