Update README.md

README.md

@@ -10,7 +10,6 @@ tags:
 - finance
 - relation_extraction
 - relation_types
-- classification
 ---
 
 
@@ -54,10 +53,23 @@ from transformers import AutoTokenizer, pipeline
 # Load Model with PEFT adapter
 
 finetune_name = 'Askinkaty/llama-finance-relations'
-tokenizer = AutoTokenizer.from_pretrained(finetune_name)
-model = AutoPeftModelForCausalLM.from_pretrained(
-    finetune_name, device_map="auto", torch_dtype=torch.float16
+
+finetined_model = AutoPeftModelForCausalLM.from_pretrained(
+    pretrained_model_name_or_path=finetune_name,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
 )
+
+
+base_model = "meta-llama/Llama-3.2-1B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+base_model.config.pad_token_id = base_model.config.eos_token_id
+
+
+pipeline = pipeline('text-generation', model=base_model, tokenizer=tokenizer)
+pipeline.model = model.to(device) 
+
+
 ```
 
 
@@ -99,31 +111,8 @@ def batch_convert_to_messages(data):
     return messages
 ```
 
-The datasets were created using the code below.
 
 
-```python
-from transformers import AutoTokenizer
-from datasets import Dataset
-
-tokenizer = AutoTokenizer.from_pretrained("meta/Llama-3.2-1B-Instruct")
-
-messages = [
-    [
-             {
-                  "role": "system",
-                  "content": "You are an expert in financial documentation and market analysis. Define relations between two specified entities: entity 1 [E1] and entity 2 [E2] in a sentence. Return a short response in the required format. "
-              },
-              {"role": "user", "content": f"{question}"},
-              {"role": "assistant", "content": f"{relation}"},
-    ], ...
-]
-
-
-dataset = Dataset.from_dict({"messages": messages})
-dataset = dataset.map(lambda x: {"formatted_chat": tokenizer.apply_chat_template(x["messages"], tokenize=False, add_generation_prompt=False)})
-
-```
 
 #### Training Hyperparameters