Use model trained with 14k examples

functions.py

@@ -286,8 +286,10 @@ def load_embeddings_model(model_ckpt: str):
     return model, tokenizer
 
 
-model, tokenizer = load_model(
-    "hackathon-somos-nlp-2023/opt-6.7b-lora-sag-t3000-v300-v2")
+# Models trained with LoRA
+# - hackathon-somos-nlp-2023/opt-6.7b-lora-sag-t3000-v300-v2
+# - hackathon-somos-nlp-2023/opt-6.7b-lora-sag-t14000-v1400-v1
+model, tokenizer = load_model("hackathon-somos-nlp-2023/opt-6.7b-lora-sag-t14000-v1400-v1")
 pipe = pipeline("text2text-generation", model=model,
                 tokenizer=tokenizer, max_new_tokens=100)
 llm = HuggingFacePipeline(pipeline=pipe)