init

app.py

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+# Adapted from https://github.com/THUDM/ChatGLM-6B/blob/main/web_demo.py
+
+import argparse
+from pathlib import Path
+
+import chatglm_cpp
+import gradio as gr
+
+import urllib
+
+DEFAULT_MODEL_PATH = "chatglm3-6b.bin"
+
+testfile = urllib.URLopener()
+testfile.retrieve(
+    "https://huggingface.co/Braddy/chatglm3-6b-chitchat/resolve/main/q5_1.bin?download=true", 
+    DEFAULT_MODEL_PATH
+    )
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-m", "--model", default=DEFAULT_MODEL_PATH, type=Path, help="model path")
+parser.add_argument("--mode", default="chat", type=str, choices=["chat", "generate"], help="inference mode")
+parser.add_argument("-l", "--max_length", default=2048, type=int, help="max total length including prompt and output")
+parser.add_argument("-c", "--max_context_length", default=512, type=int, help="max context length")
+parser.add_argument("--top_k", default=0, type=int, help="top-k sampling")
+parser.add_argument("--top_p", default=0.7, type=float, help="top-p sampling")
+parser.add_argument("--temp", default=0.95, type=float, help="temperature")
+parser.add_argument("--repeat_penalty", default=1.0, type=float, help="penalize repeat sequence of tokens")
+parser.add_argument("-t", "--threads", default=0, type=int, help="number of threads for inference")
+parser.add_argument("--plain", action="store_true", help="display in plain text without markdown support")
+args = parser.parse_args()
+
+pipeline = chatglm_cpp.Pipeline(args.model)
+
+
+def postprocess(text):
+    if args.plain:
+        return f"<pre>{text}</pre>"
+    return text
+
+
+def predict(input, chatbot, max_length, top_p, temperature, messages):
+    chatbot.append((postprocess(input), ""))
+    messages.append(chatglm_cpp.ChatMessage(role="user", content=input))
+
+    generation_kwargs = dict(
+        max_length=max_length,
+        max_context_length=args.max_context_length,
+        do_sample=temperature > 0,
+        top_k=args.top_k,
+        top_p=top_p,
+        temperature=temperature,
+        repetition_penalty=args.repeat_penalty,
+        num_threads=args.threads,
+        stream=True,
+    )
+
+    response = ""
+    if args.mode == "chat":
+        chunks = []
+        for chunk in pipeline.chat(messages, **generation_kwargs):
+            response += chunk.content
+            chunks.append(chunk)
+            chatbot[-1] = (chatbot[-1][0], postprocess(response))
+            yield chatbot, messages
+        messages.append(pipeline.merge_streaming_messages(chunks))
+    else:
+        for chunk in pipeline.generate(input, **generation_kwargs):
+            response += chunk
+            chatbot[-1] = (chatbot[-1][0], postprocess(response))
+            yield chatbot, messages
+
+    yield chatbot, messages
+
+
+def reset_user_input():
+    return gr.update(value="")
+
+
+def reset_state():
+    return [], []
+
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h1 align="center">ChatGLM.cpp</h1>""")
+
+    chatbot = gr.Chatbot()
+    with gr.Row():
+        with gr.Column(scale=4):
+            user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=8)
+            submitBtn = gr.Button("Submit", variant="primary")
+        with gr.Column(scale=1):
+            max_length = gr.Slider(0, 2048, value=args.max_length, step=1.0, label="Maximum Length", interactive=True)
+            top_p = gr.Slider(0, 1, value=args.top_p, step=0.01, label="Top P", interactive=True)
+            temperature = gr.Slider(0, 1, value=args.temp, step=0.01, label="Temperature", interactive=True)
+            emptyBtn = gr.Button("Clear History")
+
+    messages = gr.State([])
+
+    submitBtn.click(
+        predict,
+        [user_input, chatbot, max_length, top_p, temperature, messages],
+        [chatbot, messages],
+        show_progress=True,
+    )
+    submitBtn.click(reset_user_input, [], [user_input])
+
+    emptyBtn.click(reset_state, outputs=[chatbot, messages], show_progress=True)
+
+demo.queue().launch(share=False, inbrowser=True)

requirements.txt

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+openai
+tiktoken
+chatglm-cpp
+unstructured
+unstructured[local-inference]
+deepsparse-nightly[llm]