initial version with Qwen2.5-Coder-1.5B-Instruct-GGUF

app.py

@@ -0,0 +1,60 @@
+import streamlit as st
+from llama_cpp import Llama
+
+
+repo_ir = "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF"
+llm = Llama.from_pretrained(
+    repo_id=repo_ir,
+    filename="qwen2.5-coder-1.5b-instruct-q8_0.gguf",
+    verbose=True,
+    use_mmap=True,
+    use_mlock=True,
+    n_threads=4,
+    n_threads_batch=4,
+    n_ctx=8000,
+)
+print(f"{repo_ir} loaded successfully. ✅")
+
+
+# Streamed response emulator
+def response_generator(messages):
+    completion = llm.create_chat_completion(
+        messages, max_tokens=2048, stream=True, temperature=0.7, top_p=0.95
+    )
+
+    for message in completion:
+        delta = message["choices"][0]["delta"]
+        if "content" in delta:
+            yield delta["content"]
+
+
+st.title("CSV TO SQL")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("What is up?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    messages = [{"role": "system", "content": "You are a helpful assistant"}]
+
+    for val in st.session_state.messages:
+        messages.append(val)
+
+    messages.append({"role": "user", "content": prompt})
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        response = st.write_stream(response_generator(messages=messages))
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response})