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app.py

@@ -11,7 +11,7 @@ from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
 tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
 
 BASE_MODEL = "TheBloke/vicuna-7B-1.1-HF"
-LORA_WEIGHTS = "RinInori/vicuna_finetuned_6_sentiments" #Fine-tuned Vicuna model for sentiment analysis
+LORA_WEIGHTS = "RinInori/vicuna_finetuned_6_sentiments" #Fine-tuned Alpaca model for sentiment analysis
 
 if torch.cuda.is_available():
     device = "cuda"
@@ -114,9 +114,9 @@ g = gr.Interface(
     fn=evaluate,
     inputs=[
         gr.components.Textbox(
-            lines=2, label="Instruction", placeholder="Classify the text as one of these six different emotions: anger, fear, joy, love, sadness, or surprise.Response in lower-case and one word only."
+            lines=2, label="Instruction", placeholder="Type your Instruction here"
         ),
-        gr.components.Textbox(lines=2, label="Input", placeholder="I am crying"),
+        gr.components.Textbox(lines=2, label="Input", placeholder="none"),
         gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"),
         gr.components.Slider(minimum=0, maximum=1, value=0.7, label="Top p"),
         gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"),
@@ -134,7 +134,7 @@ g = gr.Interface(
     title="Fine-tuned version of Vicuna Model",
     description="This model is a fine-tuned version of the Vicuna model for sentiment analysis. https://github.com/hennypurwadi/Vicuna_finetune_sentiment_analysis \
     Base model is https://huggingface.co/TheBloke/vicuna-7B-1.1-HF \
-    It is fine-tuned and trained on a dataset to Classify the text as one of these six different emotions: anger, fear, joy, love, sadness, or surprise.Response in lower-case and one word only. \
+    It is fine-tuned and trained on a dataset to classify text as one of these six different emotions: anger, fear, joy, love, sadness, or surprise. \
     The model was trained and tested on a labeled dataset from Kaggle (https://www.kaggle.com/datasets/praveengovi/emotions-dataset-for-nlp)",
 )
 

config.json

@@ -1,24 +1,24 @@
 {
-  "_name_or_path": "/content/llama-7b-hf",
+  "_name_or_path": "/workspace/huggyllama_llama-13b",
   "architectures": [
     "LlamaForCausalLM"
   ],
-  "bos_token_id": 0,
-  "eos_token_id": 1,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
   "hidden_act": "silu",
-  "hidden_size": 4096,
+  "hidden_size": 5120,
   "initializer_range": 0.02,
-  "intermediate_size": 11008,
+  "intermediate_size": 13824,
   "max_position_embeddings": 2048,
   "max_sequence_length": 2048,
   "model_type": "llama",
-  "num_attention_heads": 32,
-  "num_hidden_layers": 32,
-  "pad_token_id": -1,
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "pad_token_id": 0,
   "rms_norm_eps": 1e-06,
   "tie_word_embeddings": false,
   "torch_dtype": "float16",
-  "transformers_version": "4.28.0.dev0",
+  "transformers_version": "4.28.1",
   "use_cache": true,
-  "vocab_size": 32000
-}
+  "vocab_size": 32001
+}