update app

eval_llm.py

@@ -13,13 +13,14 @@ def init_model(args):
     tokenizer = AutoTokenizer.from_pretrained('./model/')
     if args.load == 0:
         moe_path = '_moe' if args.use_moe else ''
-        modes = {0: 'pretrain', 1: 'full_sft', 2: 'rlhf', 3: 'reason', 4: 'grpo'}
+        modes = {0: 'pretrain', 1: 'full_sft', 2: 'rlhf', 3: 'reason', 4: 'ppo_actor', 5: 'grpo'}
         ckp = f'./{args.out_dir}/{modes[args.model_mode]}_{args.hidden_size}{moe_path}.pth'
 
         model = MiniMindForCausalLM(MiniMindConfig(
             hidden_size=args.hidden_size,
             num_hidden_layers=args.num_hidden_layers,
-            use_moe=args.use_moe
+            use_moe=args.use_moe,
+            inference_rope_scaling=args.inference_rope_scaling
         ))
 
         model.load_state_dict(torch.load(ckp, map_location=args.device), strict=True)
@@ -28,7 +29,7 @@ def init_model(args):
             apply_lora(model)
             load_lora(model, f'./{args.out_dir}/lora/{args.lora_name}_{args.hidden_size}.pth')
     else:
-        transformers_model_path = './MiniMind2'
+        transformers_model_path = './MiniMind2-MoE'
         tokenizer = AutoTokenizer.from_pretrained(transformers_model_path)
         model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True)
     print(f'MiniMind模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M(illion)')
@@ -48,8 +49,8 @@ def get_prompt_datas(args):
             '杭州市的美食有'
         ]
     else:
+        # 非LoRA模型的通用对话问题
         if args.lora_name == 'None':
-            # 通用对话问题
             prompt_datas = [
                 '请介绍一下自己。',
                 '你更擅长哪一个学科？',
@@ -62,7 +63,7 @@ def get_prompt_datas(args):
                 'Introduce the history of the United States, please.'
             ]
         else:
-            # 特定领域问题
+            # LoRA微调模型的特定领域问题
             lora_prompt_datas = {
                 'lora_identity': [
                     "你是ChatGPT吧。",
@@ -111,13 +112,14 @@ def main():
     parser.add_argument('--num_hidden_layers', default=8, type=int)
     parser.add_argument('--max_seq_len', default=8192, type=int)
     parser.add_argument('--use_moe', default=False, type=bool)
-    # 携带历史对话上下文条数
+    parser.add_argument('--model_mode', default=5, type=int, help="0: 预训练模型，1: SFT-Chat模型，2: RLHF-Chat模型，3: Reason模型，4: RLAIF-Chat模型，6: Funcall-Chat模型")
-    # history_cnt需要设为偶数，即【用户问题, 模型回答】为1组；设置为0时，即当前query不携带历史上文
+    # 启用长度外推，默认为4倍（注：仅解决位置编码外推问题，不代表模型真实具备长文本能力）
-    # 模型未经过外推微调时，在更长的上下文的chat_template时难免出现性能的明显退化，因此需要注意此处设置
+    parser.add_argument('--inference_rope_scaling', default=False, action='store_true')
+    # 携带历史对话上下文条数history_cnt需要设为偶数，即【用户问题, 模型回答】为1组；设置为0时，即当前query不携带历史上文
+    # 模型未经过多轮对话微调时，在多轮次的长上下文难免出现能力的明显退化，因此需要注意此处设置
     parser.add_argument('--history_cnt', default=0, type=int)
-    parser.add_argument('--load', default=0, type=int, help="0: 原生torch权重，1: transformers加载")
+    # load模式为1时，前置hidden_size、num_hidden_layers、max_seq_len等参数失效，即以加载的transformers模型的config.json配置为准
-    parser.add_argument('--model_mode', default=1, type=int,
+    parser.add_argument('--load', default=1, type=int, help="0: 原生torch权重，1: transformers加载")
-                        help="0: 预训练模型，1: SFT-Chat模型，2: RLHF-Chat模型，3: Reason模型，4: RLAIF-Chat模型")
     args = parser.parse_args()
 
     model, tokenizer = init_model(args)
@@ -128,18 +130,27 @@ def main():
 
     messages = []
     for idx, prompt in enumerate(prompts if test_mode == 0 else iter(lambda: input('👶: '), '')):
-        setup_seed(random.randint(0, 2048))
+        # setup_seed(random.randint(0, 2048))
-        # setup_seed(2025)  # 如需固定每次输出则换成【固定】的随机种子
+        setup_seed(2026)  # 如需固定每次输出则换成【固定】的随机种子
         if test_mode == 0: print(f'👶: {prompt}')
 
         messages = messages[-args.history_cnt:] if args.history_cnt else []
         messages.append({"role": "user", "content": prompt})
 
-        new_prompt = tokenizer.apply_chat_template(
+        # 1. Pretrain：接龙模型
-            messages,
+        if args.model_mode == 0:
-            tokenize=False,
+            new_prompt = tokenizer.bos_token + prompt
-            add_generation_prompt=True
+        # 2. SFT/RL：聊天模型
-        ) if args.model_mode != 0 else (tokenizer.bos_token + prompt)
+        else:
+            template_args = {
+                "conversation": messages,
+                "tokenize": False,
+                "add_generation_prompt": True
+            }
+            # 只可对Reason模型使用，非思考模型不能加此参数
+            if args.model_mode == 3:
+                template_args["enable_thinking"] = True  # False则关闭think
+            new_prompt = tokenizer.apply_chat_template(**template_args)
 
         inputs = tokenizer(
             new_prompt,

others/HF-Space/app.py

@@ -0,0 +1,320 @@
+import random
+import re
+from threading import Thread
+
+import torch
+import numpy as np
+import streamlit as st
+
+st.set_page_config(page_title="MiniMind", initial_sidebar_state="collapsed")
+
+st.markdown("""
+    <style>
+        /* 调整主容器边距，避免溢出 */
+        .stMainBlockContainer {
+            padding-top: 1rem !important;
+            padding-bottom: 1rem !important;
+        }
+        
+        /* 操作按钮样式 */
+        .stButton > button {
+            box-sizing: border-box !important;
+            border-radius: 50% !important;
+            width: 24px !important;
+            height: 24px !important;
+            min-width: 24px !important;
+            min-height: 24px !important;
+            max-width: 24px !important;
+            max-height: 24px !important;
+            padding: 0 !important;
+            background-color: transparent !important;
+            border: 1px solid #ddd !important;
+            display: flex !important;
+            align-items: center !important;
+            justify-content: center !important;
+            font-size: 14px !important;
+            color: #888 !important;
+            cursor: pointer !important;
+            transition: all 0.2s ease !important;
+            margin: 2px !important;
+        }
+        
+        .stButton > button:hover {
+            border-color: #999 !important;
+            color: #333 !important;
+            background-color: #f5f5f5 !important;
+        }
+        
+        /* 确保聊天容器不溢出 */
+        .stChatMessage {
+            max-width: 100% !important;
+        }
+        
+        /* 侧边栏样式优化 */
+        section[data-testid="stSidebar"] {
+            overflow-y: auto !important;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+system_prompt = []
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def process_assistant_content(content):
+    if model_source == "API" and 'R1' not in api_model_name:
+        return content
+    if model_source != "API" and 'R1' not in MODEL_PATHS[selected_model][1]:
+        return content
+
+    if '<think>' in content and '</think>' in content:
+        content = re.sub(r'(<think>)(.*?)(</think>)',
+                         r'<details style="font-style: italic; background: rgba(222, 222, 222, 0.5); padding: 10px; border-radius: 10px;"><summary style="font-weight:bold;">推理内容（展开）</summary>\2</details>',
+                         content,
+                         flags=re.DOTALL)
+
+    if '<think>' in content and '</think>' not in content:
+        content = re.sub(r'<think>(.*?)$',
+                         r'<details open style="font-style: italic; background: rgba(222, 222, 222, 0.5); padding: 10px; border-radius: 10px;"><summary style="font-weight:bold;">推理中...</summary>\1</details>',
+                         content,
+                         flags=re.DOTALL)
+
+    if '<think>' not in content and '</think>' in content:
+        content = re.sub(r'(.*?)</think>',
+                         r'<details style="font-style: italic; background: rgba(222, 222, 222, 0.5); padding: 10px; border-radius: 10px;"><summary style="font-weight:bold;">推理内容（展开）</summary>\1</details>',
+                         content,
+                         flags=re.DOTALL)
+
+    return content
+
+
+@st.cache_resource
+def load_model_tokenizer(model_path):
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        trust_remote_code=True
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_path,
+        trust_remote_code=True
+    )
+    model = model.eval().to(device)
+    return model, tokenizer
+
+
+def clear_chat_messages():
+    del st.session_state.messages
+    del st.session_state.chat_messages
+
+
+def init_chat_messages():
+    if "messages" in st.session_state:
+        for i, message in enumerate(st.session_state.messages):
+            if message["role"] == "assistant":
+                with st.chat_message("assistant", avatar=image_url):
+                    st.markdown(process_assistant_content(message["content"]), unsafe_allow_html=True)
+                    if st.button("🗑", key=f"delete_{i}"):
+                        st.session_state.messages.pop(i)
+                        st.session_state.messages.pop(i - 1)
+                        st.session_state.chat_messages.pop(i)
+                        st.session_state.chat_messages.pop(i - 1)
+                        st.rerun()
+            else:
+                st.markdown(
+                    f'<div style="display: flex; justify-content: flex-end;"><div style="display: inline-block; margin: 10px 0; padding: 8px 12px 8px 12px;  background-color: #ddd; border-radius: 10px; color: black;">{message["content"]}</div></div>',
+                    unsafe_allow_html=True)
+
+    else:
+        st.session_state.messages = []
+        st.session_state.chat_messages = []
+
+    return st.session_state.messages
+
+
+def regenerate_answer(index):
+    st.session_state.messages.pop()
+    st.session_state.chat_messages.pop()
+    st.rerun()
+
+
+def delete_conversation(index):
+    st.session_state.messages.pop(index)
+    st.session_state.messages.pop(index - 1)
+    st.session_state.chat_messages.pop(index)
+    st.session_state.chat_messages.pop(index - 1)
+    st.rerun()
+
+
+st.sidebar.title("模型设定调整")
+
+# st.sidebar.text("训练数据偏差，增加上下文记忆时\n多轮对话（较单轮）容易出现能力衰减")
+st.session_state.history_chat_num = st.sidebar.slider("Number of Historical Dialogues", 0, 6, 0, step=2)
+# st.session_state.history_chat_num = 0
+st.session_state.max_new_tokens = st.sidebar.slider("Max Sequence Length", 256, 8192, 8192, step=1)
+st.session_state.temperature = st.sidebar.slider("Temperature", 0.6, 1.2, 0.85, step=0.01)
+
+model_source = st.sidebar.radio("选择模型来源", ["本地模型", "API"], index=0)
+
+if model_source == "API":
+    api_url = st.sidebar.text_input("API URL", value="http://127.0.0.1:8000/v1")
+    api_model_id = st.sidebar.text_input("Model ID", value="minimind")
+    api_model_name = st.sidebar.text_input("Model Name", value="MiniMind2")
+    api_key = st.sidebar.text_input("API Key", value="none", type="password")
+    slogan = f"Hi, I'm {api_model_name}"
+else:
+    MODEL_PATHS = {
+        "MiniMind2 (0.1B)": ["./MiniMind2", "MiniMind2"],
+        "MiniMind2-MoE (0.15B)": ["./MiniMind2-MoE", "MiniMind2-MoE"],
+        "MiniMind2-Small (0.02B)": ["./MiniMind2-Small", "MiniMind2-Small"]
+    }
+
+    selected_model = st.sidebar.selectbox('Models', list(MODEL_PATHS.keys()), index=0)  # 默认选择 MiniMind2
+    model_path = MODEL_PATHS[selected_model][0]
+    slogan = f"Hi, I'm {MODEL_PATHS[selected_model][1]}"
+
+image_url = "https://www.modelscope.cn/api/v1/studio/gongjy/MiniMind/repo?Revision=master&FilePath=images%2Flogo2.png&View=true"
+
+st.markdown(
+    f'<div style="display: flex; flex-direction: column; align-items: center; text-align: center; margin: 0; padding: 0;">'
+    '<div style="font-style: italic; font-weight: 900; margin: 0; padding-top: 4px; display: flex; align-items: center; justify-content: center; flex-wrap: wrap; width: 100%;">'
+    f'<img src="{image_url}" style="width: 45px; height: 45px; "> '
+    f'<span style="font-size: 26px; margin-left: 10px;">{slogan}</span>'
+    '</div>'
+    '<span style="color: #bbb; font-style: italic; margin-top: 6px; margin-bottom: 10px;">内容完全由AI生成，请务必仔细甄别<br>Content AI-generated, please discern with care</span>'
+    '</div>',
+    unsafe_allow_html=True
+)
+
+
+def setup_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+def main():
+    if model_source == "本地模型":
+        model, tokenizer = load_model_tokenizer(model_path)
+    else:
+        model, tokenizer = None, None
+
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+        st.session_state.chat_messages = []
+
+    messages = st.session_state.messages
+
+    for i, message in enumerate(messages):
+        if message["role"] == "assistant":
+            with st.chat_message("assistant", avatar=image_url):
+                st.markdown(process_assistant_content(message["content"]), unsafe_allow_html=True)
+                if st.button("×", key=f"delete_{i}"):
+                    st.session_state.messages = st.session_state.messages[:i - 1]
+                    st.session_state.chat_messages = st.session_state.chat_messages[:i - 1]
+                    st.rerun()
+        else:
+            st.markdown(
+                f'<div style="display: flex; justify-content: flex-end;"><div style="display: inline-block; margin: 10px 0; padding: 8px 12px 8px 12px;  background-color: gray; border-radius: 10px; color:white; ">{message["content"]}</div></div>',
+                unsafe_allow_html=True)
+
+    prompt = st.chat_input(key="input", placeholder="给 MiniMind 发送消息")
+
+    if hasattr(st.session_state, 'regenerate') and st.session_state.regenerate:
+        prompt = st.session_state.last_user_message
+        regenerate_index = st.session_state.regenerate_index
+        delattr(st.session_state, 'regenerate')
+        delattr(st.session_state, 'last_user_message')
+        delattr(st.session_state, 'regenerate_index')
+
+    if prompt:
+        st.markdown(
+            f'<div style="display: flex; justify-content: flex-end;"><div style="display: inline-block; margin: 10px 0; padding: 8px 12px 8px 12px;  background-color: gray; border-radius: 10px; color:white; ">{prompt}</div></div>',
+            unsafe_allow_html=True)
+        messages.append({"role": "user", "content": prompt[-st.session_state.max_new_tokens:]})
+        st.session_state.chat_messages.append({"role": "user", "content": prompt[-st.session_state.max_new_tokens:]})
+
+        with st.chat_message("assistant", avatar=image_url):
+            placeholder = st.empty()
+
+            if model_source == "API":
+                try:
+                    from openai import OpenAI
+
+                    client = OpenAI(
+                        api_key=api_key,
+                        base_url=api_url
+                    )
+                    history_num = st.session_state.history_chat_num + 1  # +1 是为了包含当前的用户消息
+                    conversation_history = system_prompt + st.session_state.chat_messages[-history_num:]
+                    answer = ""
+                    response = client.chat.completions.create(
+                        model=api_model_id,
+                        messages=conversation_history,
+                        stream=True,
+                        temperature=st.session_state.temperature
+                    )
+
+                    for chunk in response:
+                        content = chunk.choices[0].delta.content or ""
+                        answer += content
+                        placeholder.markdown(process_assistant_content(answer), unsafe_allow_html=True)
+
+                except Exception as e:
+                    answer = f"API调用出错: {str(e)}"
+                    placeholder.markdown(answer, unsafe_allow_html=True)
+            else:
+                random_seed = random.randint(0, 2 ** 32 - 1)
+                setup_seed(random_seed)
+
+                st.session_state.chat_messages = system_prompt + st.session_state.chat_messages[
+                                                                 -(st.session_state.history_chat_num + 1):]
+                new_prompt = tokenizer.apply_chat_template(
+                    st.session_state.chat_messages,
+                    tokenize=False,
+                    add_generation_prompt=True
+                )
+
+                inputs = tokenizer(
+                    new_prompt,
+                    return_tensors="pt",
+                    truncation=True
+                ).to(device)
+
+                streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+                generation_kwargs = {
+                    "input_ids": inputs.input_ids,
+                    "max_length": inputs.input_ids.shape[1] + st.session_state.max_new_tokens,
+                    "num_return_sequences": 1,
+                    "do_sample": True,
+                    "attention_mask": inputs.attention_mask,
+                    "pad_token_id": tokenizer.pad_token_id,
+                    "eos_token_id": tokenizer.eos_token_id,
+                    "temperature": st.session_state.temperature,
+                    "top_p": 0.85,
+                    "streamer": streamer,
+                }
+
+                Thread(target=model.generate, kwargs=generation_kwargs).start()
+
+                answer = ""
+                for new_text in streamer:
+                    answer += new_text
+                    placeholder.markdown(process_assistant_content(answer), unsafe_allow_html=True)
+
+            messages.append({"role": "assistant", "content": answer})
+            st.session_state.chat_messages.append({"role": "assistant", "content": answer})
+            with st.empty():
+                if st.button("×", key=f"delete_{len(messages) - 1}"):
+                    st.session_state.messages = st.session_state.messages[:-2]
+                    st.session_state.chat_messages = st.session_state.chat_messages[:-2]
+                    st.rerun()
+
+
+if __name__ == "__main__":
+    from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+    main()