Update app.py

app.py

@@ -3,13 +3,31 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
 # Set model ID
-# comment out the model you want to use
-# model_id = "gpt2" # for testing purposes only
-# model_id = "deepseek-ai/deepseek-coder-1.3b"
-# model_id = "deepseek-ai/deepseek-coder-1.3b-base"
-model_id = "deepseek-ai/deepseek-coder-1.3b-instruct"
+# comment/uncomment the model you want to use
+
+# GPT-2 (very small, general-purpose, mainly for testing or learning purposes)
+# model_id = "gpt2"  
+
+# DeepSeek Coder 1.3B (base version, no instruction fine-tuning — better for raw code generation tasks)
+# model_id = "deepseek-ai/deepseek-coder-1.3b"  
+
+# DeepSeek Coder 1.3B Base (same as above — explicit base naming, safe to use)
+# model_id = "deepseek-ai/deepseek-coder-1.3b-base"  
 
+# CodeLlama 7B Instruct (powerful code generation model from Meta, instruction-tuned)
+# model_id = "codellama/CodeLlama-7b-Instruct-hf"  
 
+# Meta-Llama 3.1 8B Instruct (very powerful general-purpose model, instruction-following, also decent for code & NLP)
+# model_id = "meta-llama/Llama-3.1-8B-Instruct"  
+
+# DeepSeek-R1 + Qwen3 8B (highly capable multi-purpose model — great for reasoning, coding, general Q&A)
+# model_id = "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B"  
+
+# Qwen2.5-VL-7B Instruct (multimodal: can handle text + images, instruction-tuned — mostly for vision-language tasks)
+# model_id = "Qwen/Qwen2.5-VL-7B-Instruct"  
+
+# DeepSeek Coder 1.3B Instruct (great for both natural language and coding tasks)
+model_id = "deepseek-ai/deepseek-coder-1.3b-instruct"
 # Load tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
@@ -30,7 +48,7 @@ def generate_code(prompt):
     with torch.no_grad():
         outputs = model.generate(
             **inputs,
-            max_new_tokens=256,
+            max_new_tokens=200,
             temperature=0.7
         )