update minimind-v1 update readme

fast_infenence.py

@@ -0,0 +1,128 @@
+import json
+import streamlit as st
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation.utils import GenerationConfig
+
+st.set_page_config(page_title="MiniMind-V1 Demo(无历史上文)")
+st.title("MiniMind-V1 Demo(无历史上文)")
+
+model_id = "minimind-v1"
+
+# -----------------------------------------------------------------------------
+temperature = 0.7
+top_k = 8
+max_seq_len = 1 * 1024
+# -----------------------------------------------------------------------------
+
+
+@st.cache_resource
+def load_model_tokenizer():
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        trust_remote_code=True
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_id,
+        use_fast=False,
+        trust_remote_code=True
+    )
+    model = model.eval()
+    generation_config = GenerationConfig.from_pretrained(model_id)
+    return model, tokenizer, generation_config
+
+
+def clear_chat_messages():
+    del st.session_state.messages
+
+
+def init_chat_messages():
+    with st.chat_message("assistant", avatar='🤖'):
+        st.markdown("您好，我是由Joya开发的MiniMind，很高兴为您服务😄")
+
+    if "messages" in st.session_state:
+        for message in st.session_state.messages:
+            avatar = "🧑‍💻" if message["role"] == "user" else "🤖"
+            with st.chat_message(message["role"], avatar=avatar):
+                st.markdown(message["content"])
+    else:
+        st.session_state.messages = []
+
+    return st.session_state.messages
+
+
+# max_new_tokens = st.sidebar.slider("max_new_tokens", 0, 1024, 512, step=1)
+# top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01)
+# top_k = st.sidebar.slider("top_k", 0, 100, 0, step=1)
+# temperature = st.sidebar.slider("temperature", 0.0, 2.0, 1.0, step=0.01)
+# do_sample = st.sidebar.checkbox("do_sample", value=False)
+
+
+def main():
+    model, tokenizer, generation_config = load_model_tokenizer()
+    messages = init_chat_messages()
+
+    if prompt := st.chat_input("Shift + Enter 换行, Enter 发送"):
+        with st.chat_message("user", avatar='🧑‍💻'):
+            st.markdown(prompt)
+            messages.append({"role": "user", "content": prompt})
+        with st.chat_message("assistant", avatar='🤖'):
+            placeholder = st.empty()
+
+            chat_messages = []
+            chat_messages.append({"role": "user", "content": prompt})
+            # print(messages)
+            new_prompt = tokenizer.apply_chat_template(
+                chat_messages,
+                tokenize=False,
+                add_generation_prompt=True
+            )[-(max_seq_len - 1):]
+
+            x = tokenizer(new_prompt).data['input_ids']
+            x = (torch.tensor(x, dtype=torch.long)[None, ...])
+
+            response = ''
+
+            with torch.no_grad():
+                res_y = model.generate(x, tokenizer.eos_token_id, max_new_tokens=max_seq_len, temperature=temperature,
+                                       top_k=top_k, stream=True)
+                try:
+                    y = next(res_y)
+                except StopIteration:
+                    return
+
+                history_idx = 0
+                while y != None:
+                    answer = tokenizer.decode(y[0].tolist())
+                    if answer and answer[-1] == '<EFBFBD>':
+                        try:
+                            y = next(res_y)
+                        except:
+                            break
+                        continue
+                    # print(answer)
+                    if not len(answer):
+                        try:
+                            y = next(res_y)
+                        except:
+                            break
+                        continue
+                    placeholder.markdown(answer)
+                    response = answer
+                    try:
+                        y = next(res_y)
+                    except:
+                        break
+
+            # if contain_history_chat:
+            #     assistant_answer = answer.replace(new_prompt, "")
+            #     messages.append({"role": "assistant", "content": assistant_answer})
+
+        messages.append({"role": "assistant", "content": response})
+        # print("messages: ", json.dumps(response, ensure_ascii=False), flush=True)
+
+    st.button("清空对话", on_click=clear_chat_messages)
+
+
+if __name__ == "__main__":
+    main()