solve a minor bug

src/app.py

@@ -52,6 +52,19 @@ def get_name(index):
 
 gpu_specs = get_gpu_specs()
 
+_, col, _ = st.columns([1,3,1])
+with col.expander("Information", expanded=True):
+    st.markdown("""- GPU information comes from [TechPowerUp GPU Specs](https://www.techpowerup.com/gpu-specs/)
+- Mainly based on [Model Memory Calculator by hf-accelerate](https://huggingface.co/spaces/hf-accelerate/model-memory-usage)
+    using `transformers` library
+- Inference is calculated following [EleutherAI Transformer Math 101](https://blog.eleuther.ai/transformer-math/),
+    where is estimated as """)
+    
+    st.latex(r"""\text{Memory}_\text{Inference} \approx \text{Model Size} \times 1.2""")
+    st.markdown("""- For LoRa Fine-tuning, I'm asuming a **16-bit** dtype of trainable parameters. The formula (in terms of GB) is""")
+    st.latex(r"\text{Memory}_\text{LoRa} \approx \text{Model Size} + \left(\text{ \# trainable Params}_\text{Billions}\times\frac{16}{8} \times 4\right) \times 1.2")
+    st.markdown("- You can understand `int4` as models in `GPTQ-4bit`, `AWQ-4bit` or `Q4_0 GGUF/GGML` formats")
+
 access_token = st.sidebar.text_input("Access token")
 model_name = st.sidebar.text_input("Model name", value="mistralai/Mistral-7B-v0.1")
 if not model_name:
@@ -84,6 +97,9 @@ min_ram = gpu_info['RAM (GB)'].min()
 max_ram = gpu_info['RAM (GB)'].max()
 ram = st.sidebar.slider("Filter by RAM (GB)", min_ram, max_ram, (10.0, 40.0), step=0.5)
 gpu_info = gpu_info[gpu_info["RAM (GB)"].between(ram[0], ram[1])]
+if len(gpu_info) == 0:
+    st.sidebar.error(f"**{gpu_vendor}** has no GPU in that RAM range")
+    st.stop()
 gpu = st.sidebar.selectbox("GPU", gpu_info['Product Name'].index.tolist(), format_func=lambda x : gpu_specs.iloc[x]['Product Name'])
 gpu_spec = gpu_specs.iloc[gpu]
 gpu_spec.name = 'INFO'
@@ -95,20 +111,6 @@ st.sidebar.dataframe(gpu_spec.T.astype(str))
 memory_table = pd.DataFrame(st.session_state[model_name]).set_index('dtype')
 memory_table['LoRA Fine-Tuning (GB)'] = (memory_table["Total Size (GB)"] + 
                                           (memory_table["Parameters (Billion)"]* lora_pct/100 * (16/8)*4)) * 1.2
-
-_, col, _ = st.columns([1,3,1])
-with col.expander("Information", expanded=True):
-    st.markdown("""- GPU information comes from [TechPowerUp GPU Specs](https://www.techpowerup.com/gpu-specs/)
-- Mainly based on [Model Memory Calculator by hf-accelerate](https://huggingface.co/spaces/hf-accelerate/model-memory-usage)
-    using `transformers` library
-- Inference is calculated following [EleutherAI Transformer Math 101](https://blog.eleuther.ai/transformer-math/),
-    where is estimated as """)
-    
-    st.latex(r"""\text{Memory}_\text{Inference} \approx \text{Model Size} \times 1.2""")
-    st.markdown("""- For LoRa Fine-tuning, I'm asuming a **16-bit** dtype of trainable parameters. The formula (in terms of GB) is""")
-    st.latex(r"\text{Memory}_\text{LoRa} \approx \text{Model Size} + \left(\text{ \# trainable Params}_\text{Billions}\times\frac{16}{8} \times 4\right) \times 1.2")
-    st.markdown("- You can understand `int4` as models in `GPTQ-4bit`, `AWQ-4bit` or `Q4_0 GGUF/GGML` formats")
-
     
 _memory_table = memory_table.copy()
 memory_table = memory_table.round(2).T