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Model: XingChina/ChunMengDie-1.0-0.4b
Source: Original Platform

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+chunmengdie_f16\[1\].gguf filter=lfs diff=lfs merge=lfs -text
+chunmengdie_q4_km\[1\].gguf filter=lfs diff=lfs merge=lfs -text
+chunmengdie_q8_0\[1\].gguf filter=lfs diff=lfs merge=lfs -text

LICENSE

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+Copyright (c) 2026, XingChina
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice,
+   this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.

README.md

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+---
+license: bsd-3-clause
+datasets:
+- BelleGroup/train_0.5M_CN
+- liumindmind/NekoQA-10K
+- cyberlangke/Nana-catgirl-dataset-110k
+- XingChina/ChunMengDie-1.0-User-Data
+language:
+- zh
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- chinese
+- instruction-tuning
+- conversational
+- experimental
+- safetensors
+- gguf
+- quantized
+- 8bit
+- 4bit
+- llama.cpp
+---
+
+# ChunMengDie-1.0-0.4B
+
+## 关于作者
+
+一个热爱 AI 的八年级学生。欢迎交流学习。
+
+## 模型简介
+
+ChunMengDie-1.0-0.4B 是一个基于 Transformer 架构的中文对话实验模型，参数量 4 亿（0.4B）。
+
+**当前版本状态**：
+- 早期研究阶段，输出质量不稳定，存在大量乱码和语义不连贯现象。
+- 仅用于学术探索和实验验证，**不建议用于任何生产环境**。
+- 后续版本将持续迭代优化。
+- 由于其他分词器死活下不下来，使用内置的gpt2分词器进行训练（我自己尝试过训练自定义分词器，但是llama.cpp转换出现错误，尝试把分词器加进llama.cpp也不行，所以选择内置的分词器）
+- 使用动态学习率
+
+---
+
+## 📂 仓库文件结构
+
+本仓库同时提供 **Hugging Face 原生格式** 和 **llama.cpp GGUF 量化格式** 两种权重，请根据你的部署场景选择：
+
+### 🤗 Hugging Face 格式（适用于 `transformers` 库）
+
+| 文件名 | 说明 |
+|--------|------|
+| `config.json` | 模型结构配置文件 |
+| `generation_config.json` | 文本生成参数配置 |
+| `model.safetensors` | BF16 原始精度权重（约 0.8 GB） |
+| `tokenizer.json` | 分词器词表 |
+| `tokenizer_config.json` | 分词器配置 |
+
+### 🦙 llama.cpp GGUF 格式（适用于 `llama.cpp` 或 `ollama` 部署）
+
+| 精度 | 文件名 | 文件大小 | 适用场景 |
+|------|--------|----------|----------|
+| **BF16（原始）** | `chunmengdie_f16.gguf` | 501 MB | 追求最佳效果，适合高端显卡 |
+| **8bit 量化** | `chunmengdie_q8_0.gguf` | 275 MB | 常规推理，质量损失极小（推荐） |
+| **4bit 量化** | `chunmengdie_q4_km.gguf` | 181 MB | 低显存设备（如 4GB 显卡）或移动端部署 |
+
+> 💡 **选择建议**：如果你是 Python 开发者，用 `transformers` 加载 HF 格式最方便；如果你在本地命令行或边缘设备部署，用 GGUF 格式配合 `llama.cpp` 更高效。
+
+---
+
+## 🚀 推理使用指南
+
+### 方式一：Transformers（HF 格式）
+
+安装依赖：
+```bash
+pip install transformers torch
+```
+
+加载模型并生成文本：
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("XingChina/ChunMengDie-1.0-0.4B")
+tokenizer = AutoTokenizer.from_pretrained("XingChina/ChunMengDie-1.0-0.4B")
+
+inputs = tokenizer("你好", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0]))
+```
+
+### 方式二：llama.cpp（GGUF 格式）
+
+下载对应的 `.gguf` 文件后，使用 `llama.cpp` 进行推理：
+
+```bash
+# BF16 原始版本
+./main -m chunmengdie_f16.gguf -p "你好" -n 128
+
+# 8bit 量化版本
+./main -m chunmengdie_q8_0.gguf -p "你好" -n 128
+
+# 4bit 量化版本（低显存首选）
+./main -m chunmengdie_q4_km.gguf -p "你好" -n 128
+```
+
+**使用 ollama 部署（可选）**：
+```bash
+ollama create chunmengdie -f ./Modelfile
+ollama run chunmengdie
+```
+
+---
+
+## 🛡️ 训练环境与权重状态声明
+
+本模型基于 **NVIDIA RTX Pro 6000（96GB 显存）** 专业卡训练，采用针对大显存优化的高吞吐配置（大 Batch Size + 长序列）。
+
+**发布权重说明**：
+1. 本仓库仅提供 **纯推理权重**（HF 格式 + GGUF 格式），**不包含优化器（Optimizer）和调度器（Scheduler）状态**。
+2. 由于训练环境依赖 **96GB 显存级的内存分配策略**，在消费级显卡（如 24GB 4090）或标准 A100（80GB）上**无法直接加载续训**。（由于我手滑删了最终调度器，你拿什么卡都不能续训，即使能继续训也很容易爆显存并且发挥不出最大实力，中间调度器不能和最终模型混用，没有上传）
+3. 如需进行二次微调，建议使用本仓库的 BF16 权重作为基座，自行挂载新的优化器开始训练。
+4. 项目根目录train[1].py已针对20~24G显存显卡进行微调，实测在4090上全程不会爆显存，请以最新版train[1].py为准
+
+**我们鼓励二次创新，但尊重算力投入，请合理使用开源资源。**
+
+---
+
+## 📚 训练数据声明
+
+本模型在训练过程中参考/使用了以下公开数据集进行统计学习（点击可查看原始版权信息）：
+
+| 数据集 | 许可证 | 使用方式 | 版权归属 |
+|--------|--------|----------|----------|
+| [BelleGroup/train_0.5M_CN](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) | GPL-3.0 | 全量用于训练并通过统计学习从数据中提取对话模式 | 版权归 BelleGroup 所有 |
+| [liumindmind/NekoQA-10K](https://huggingface.co/datasets/liumindmind/NekoQA-10K) | Apache-2.0 | 全量用于训练并通过统计学习从数据中提取对话模式 | 版权归 MindsRiverPonder 所有 |
+| [cyberlangke/Nana-catgirl-dataset-110k](https://huggingface.co/datasets/cyberlangke/Nana-catgirl-dataset-110k) | MIT | 全量用于训练并通过统计学习从数据中提取对话模式 | 版权归 cyberlangke 所有 |
+| [XingChina/ChunMengDie-1.0-User-Data](https://huggingface.co/datasets/XingChina/ChunMengDie-1.0-User-Data) | CC BY 4.0 | 本数据集为原创角色对话数据，全量用于训练并通过统计学习从数据中提取对话模式 | 版权归 XingChina 所有 |
+
+**重要说明**：
+1. 本模型权重**并非**上述任何数据集的“衍生代码副本”或“复制品”，模型仅通过梯度下降从数据中提取统计模式。
+2. 本模型的输出内容由 AI 随机生成，**不代表对训练数据集的检索或分发**。
+3. 在极低概率下（<0.1%），模型可能因统计噪声而产生与训练数据某一段落高度相似的文本，该现象属于概率性巧合，建议使用者对输出进行抽样审核。
+4. 验证loss与训练loss均已降到1以下，但不能理解语义
+
+---
+
+## ⚠️ 免责声明
+
+本模型按“原样”（AS IS）提供，**不附带任何形式明示或默示的担保**。
+
+使用者应自行承担使用本模型产生的一切后果，包括但不限于：
+- 输出的准确性、安全性、合规性；
+- 对第三方知识产权的潜在侵犯（若发生，属极小概率事件，我方不承担责任）。
+
+建议在生产环境部署前配合敏感词过滤和输出重复检测模块。
+
+---
+
+## 🚫 使用限制
+
+- 本模型目前质量不佳，**不建议用于任何生产环境**。
+- 商业使用需自行评估输出内容的合规性。
+
+---
+
+## 📄 许可证
+
+本模型权重采用 **BSD 3-Clause License** 发布。详见 [LICENSE](./LICENSE) 文件。
+
+---
+
+## 📖 引用
+
+如果你在研究中使用本模型，请引用：
+
+```bibtex
+@misc{ChunMengDie-1.0-0.4B,
+  author = {XingChina},
+  title = {ChunMengDie-1.0-0.4B: A Chinese Conversational AI Model},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/XingChina/ChunMengDie-1.0-0.4B}
+}
+```
+
+---
+
+**感谢你对 ChunMengDie 项目的关注！** 🎉

chunmengdie_f16[1].gguf

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chunmengdie_q4_km[1].gguf

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chunmengdie_q8_0[1].gguf

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config.json

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+{
+  "activation_function": "gelu_new",
+  "add_cross_attention": false,
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.3,
+  "bos_token_id": 50256,
+  "dtype": "float32",
+  "embd_pdrop": 0.3,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 4096,
+  "n_embd": 1024,
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 16,
+  "n_positions": 4096,
+  "pad_token_id": null,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.3,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "vocab_size": 50257
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "transformers_version": "5.5.0",
+  "use_cache": true
+}

model.safetensors

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+size 1028816568

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}

train[1].py

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+#!/usr/bin/env python3
+# Copyright (c) 2026 XingChina
+# SPDX-License-Identifier: BSD-3-Clause
+# 本代码采用 BSD 3-Clause 许可证，详见项目根目录的 LICENSE 文件。
+# 为4090显卡进行优化，保证普通显卡也能训练（而不是找大显存显卡）
+# 已经拿4090测试过，全程没有崩溃
+
+import os
+import json
+import signal
+import sys
+import torch
+from transformers import (
+    GPT2Config,
+    GPT2LMHeadModel,
+    GPT2Tokenizer,
+    Trainer,
+    TrainingArguments,
+    DataCollatorForLanguageModeling,
+)
+from datasets import Dataset
+from bitsandbytes.optim import Adam8bit
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# ================== 模型配置 ==================
+MODEL_CONFIG = {
+    "n_embd": 1024,
+    "n_layer": 16,
+    "n_head": 16,
+    "n_positions": 4096,
+    "vocab_size": 50257,
+    "n_ctx": 4096,
+    "resid_pdrop": 0.3,
+    "embd_pdrop": 0.3,
+    "attn_pdrop": 0.3,
+}
+
+# ================== 训练参数（为4090优化，保证普通显卡能复现） ==================
+TRAIN_ARGS = {
+    "output_dir": "./checkpoints_chunmengdie_gpt2",
+    "per_device_train_batch_size": 2,
+    "gradient_accumulation_steps": 8,
+    "num_train_epochs": 1,
+    "learning_rate": 3e-4,
+    "weight_decay": 0.01,
+    "warmup_steps": 500,
+    "logging_steps": 10,
+    "save_steps": 500,
+    "save_total_limit": 3,
+    "bf16": True,
+    "report_to": "none",
+    "dataloader_num_workers": 0,
+    "eval_strategy": "steps",
+    "eval_steps": 500,
+    "load_best_model_at_end": True,
+    "metric_for_best_model": "eval_loss",
+    "greater_is_better": False,
+    "gradient_checkpointing": True,
+}
+
+MAX_SEQ_LEN = 4096
+CHECKPOINT_DIR = TRAIN_ARGS["output_dir"]
+
+# ====== 数据文件列表（这里假设数据已经清理过，实际用的数据确实清理过） ======
+# mengdie_train由于包含其他数据集，没有开源
+DATA_FILES = [
+    "mengdie_train.json",
+    "Belle_open_source_0.5M.json",
+]
+
+# ================== 数据加载 ==================
+def load_data(file_list):
+    texts = []
+    for fpath in file_list:
+        if not os.path.exists(fpath):
+            logger.warning(f"File not found: {fpath}, skipped")
+            continue
+        with open(fpath, 'r', encoding='utf-8') as f:
+            first_char = f.read(1)
+            f.seek(0)
+            if first_char == '[':
+                data = json.load(f)
+                logger.info(f"Loaded {len(data)} samples from {fpath} (JSON array)")
+            else:
+                data = []
+                for line in f:
+                    line = line.strip()
+                    if line:
+                        try:
+                            data.append(json.loads(line))
+                        except json.JSONDecodeError:
+                            pass
+                logger.info(f"Loaded {len(data)} samples from {fpath} (JSONL)")
+
+        for item in data:
+            try:
+                if "instruction" in item and "input" in item and "output" in item:
+                    inst = item["instruction"].strip()
+                    inp = item.get("input", "").strip()
+                    out = item["output"].strip()
+                    user_text = f"{inst}\n{inp}" if inp else inst
+                    assistant_text = out
+                elif "input" in item and "output" in item:
+                    user_text = item["input"].strip()
+                    assistant_text = item["output"].strip()
+                else:
+                    continue
+                texts.append(f"用户：{user_text}\n猫娘：{assistant_text}")
+            except Exception:
+                continue
+    logger.info(f"Total texts: {len(texts)}")
+    return texts
+
+# ================== Tokenization==================
+def tokenize_function(examples):
+    tokenized = tokenizer(
+        examples["text"],
+        truncation=True,
+        max_length=MAX_SEQ_LEN - 1,
+        padding=False,
+        return_attention_mask=False,
+    )
+    tokenized["input_ids"] = [ids + [tokenizer.eos_token_id] for ids in tokenized["input_ids"]]
+    return tokenized
+
+# ================== 紧急保存 ==================
+def emergency_save(sig, frame):
+    logger.info("\n🛑 Saving emergency checkpoint...")
+    try:
+        torch.cuda.synchronize()
+        model_to_save = model.cpu()
+        os.makedirs(CHECKPOINT_DIR, exist_ok=True)
+        model_to_save.save_pretrained(os.path.join(CHECKPOINT_DIR, "emergency"))
+        tokenizer.save_pretrained(os.path.join(CHECKPOINT_DIR, "emergency"))
+        logger.info("✅ Emergency checkpoint saved.")
+    except Exception as e:
+        logger.error(f"Emergency save failed: {e}")
+    sys.exit(0)
+
+# ================== 主程序 ==================
+if __name__ == "__main__":
+    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+    tokenizer.pad_token = tokenizer.eos_token
+
+    raw_texts = load_data(DATA_FILES)
+    dataset = Dataset.from_dict({"text": raw_texts})
+
+    tokenized_dataset = dataset.map(
+        tokenize_function,
+        batched=True,
+        remove_columns=["text"],
+        num_proc=4,
+        load_from_cache_file=True,
+    )
+
+    # 验证 EOS 是否添加成功
+    sample_ids = tokenized_dataset[0]["input_ids"]
+    logger.info(f"Sample last 5 tokens: {sample_ids[-5:]}")
+    logger.info(f"Last token is EOS: {sample_ids[-1] == tokenizer.eos_token_id}")
+
+    # 9:1 划分
+    total_len = len(tokenized_dataset)
+    train_size = int(0.9 * total_len)
+    eval_size = total_len - train_size
+    train_dataset, eval_dataset = torch.utils.data.random_split(
+        tokenized_dataset, [train_size, eval_size]
+    )
+    logger.info(f"Train: {train_size}, Eval: {eval_size}")
+
+    logger.info("Initializing GPT-2 model from scratch...")
+    config = GPT2Config(**MODEL_CONFIG)
+    model = GPT2LMHeadModel(config)
+
+    optimizer = Adam8bit(model.parameters(), lr=TRAIN_ARGS["learning_rate"])
+
+    training_args = TrainingArguments(
+        output_dir=CHECKPOINT_DIR,
+        per_device_train_batch_size=TRAIN_ARGS["per_device_train_batch_size"],
+        gradient_accumulation_steps=TRAIN_ARGS["gradient_accumulation_steps"],
+        num_train_epochs=TRAIN_ARGS["num_train_epochs"],
+        learning_rate=TRAIN_ARGS["learning_rate"],
+        weight_decay=TRAIN_ARGS["weight_decay"],
+        warmup_steps=TRAIN_ARGS["warmup_steps"],
+        logging_steps=TRAIN_ARGS["logging_steps"],
+        save_steps=TRAIN_ARGS["save_steps"],
+        save_total_limit=TRAIN_ARGS["save_total_limit"],
+        bf16=TRAIN_ARGS["bf16"],
+        report_to=TRAIN_ARGS["report_to"],
+        dataloader_num_workers=TRAIN_ARGS["dataloader_num_workers"],
+        optim="adamw_8bit",
+        eval_strategy=TRAIN_ARGS["eval_strategy"],
+        eval_steps=TRAIN_ARGS["eval_steps"],
+        load_best_model_at_end=TRAIN_ARGS["load_best_model_at_end"],
+        metric_for_best_model=TRAIN_ARGS["metric_for_best_model"],
+        greater_is_better=TRAIN_ARGS["greater_is_better"],
+        gradient_checkpointing=TRAIN_ARGS["gradient_checkpointing"],
+    )
+
+    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        data_collator=data_collator,
+        optimizers=(optimizer, None),
+    )
+
+    signal.signal(signal.SIGINT, emergency_save)
+
+    # 自动恢复最新 checkpoint（中间checkpoint保留调度器，最终权重无调度器，这是手滑删了调度器的主要原因）
+    latest_checkpoint = None
+    if os.path.exists(CHECKPOINT_DIR):
+        checkpoints = [d for d in os.listdir(CHECKPOINT_DIR) if d.startswith("checkpoint-")]
+        if checkpoints:
+            latest_checkpoint = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))[-1]
+            latest_checkpoint = os.path.join(CHECKPOINT_DIR, latest_checkpoint)
+            logger.info(f"✅ Found checkpoint: {latest_checkpoint}, will resume from there.")
+        else:
+            if os.path.exists(os.path.join(CHECKPOINT_DIR, "emergency")):
+                latest_checkpoint = os.path.join(CHECKPOINT_DIR, "emergency")
+                logger.info(f"✅ Found emergency checkpoint, will resume from there.")
+    else:
+        os.makedirs(CHECKPOINT_DIR, exist_ok=True)
+
+    logger.info("🚀 Starting training...")
+    trainer.train(resume_from_checkpoint=latest_checkpoint)
+
+    final_path = os.path.join(CHECKPOINT_DIR, "final_model")
+    model.save_pretrained(final_path)
+    tokenizer.save_pretrained(final_path)
+    logger.info(f"✅ Final model saved to {final_path}")
+    logger.info("📌 Convert to GGUF: python /path/to/llama.cpp/convert_hf_to_gguf.py ./checkpoints_chunmengdie_gpt2/final_model --outfile model.gguf --outtype q8_0")