First commit.

Contributors.md

+# Contributors
+None
\ No newline at end of file

LICENSE

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README.md

+# GLM-Z1
+## 论文
+暂无
+
+## 模型结构
+基于transformer结构
+<div align=center>
+    <img src="./doc/transformers.jpg"/>
+</div>
+
+## 算法原理
+GLM-Z1-32B-0414 是具有深度思考能力的推理模型，这是在 GLM-4-32B-0414 的基础上，通过冷启动和扩展强化学习，以及在数学、代码和逻辑等任务上对模型的进一步训练得到的。相对于基础模型，GLM-Z1-32B-0414 显著提升了数理能力和解决复杂任务的能力。在训练的过程中，引入了基于对战排序反馈的通用强化学习，进一步增强了模型的通用能力。
+
+GLM-Z1-Rumination-32B-0414 是具有沉思能力的深度推理模型（对标 Open AI 的 Deep Research）。不同于一般的深度思考模型，沉思模型通过更长时间的深度思考来解决更开放和复杂的问题（例如：撰写两个城市AI发展对比情况，以及未来的发展规划），沉思模型在深度思考过程中结合搜索工具处理复杂任务，并经过利用多种规则型奖励来指导和扩展端到端强化学习训练得到。Z1-Rumination 在研究型写作和复杂检索任务上的能力得到了显著提升。
+
+最后，GLM-Z1-9B-0414 是一个惊喜。沿用上述一系列技术，训练了一个保持开源传统的 9B 小尺寸模型。尽管规模更小，GLM-Z1-9B-0414 在数学推理和通用任务中依然展现出极为优秀的能力，其整体表现已处于同尺寸开源模型中的领先水平。特别是在资源受限的场景下，该模型在效率与效果之间实现了出色的平衡，为追求轻量化部署的用户提供了强有力的选择。
+
+效果展示
+<div align=center>
+    <img src="./doc/Bench-Z1-9B.png"/>
+    <img src="./doc/Bench-Z1-32B.png"/>
+</div>
+
+## 环境配置
+`-v 路径`、`docker_nam`e和`imageID`根据实际情况修改
+
+### Docker（方法一）
+```bash
+dcoker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+cd /your_code_path/glm-4-z1_pytorch
+pip install transformers>=4.51.3
+```
+
+### Dockerfile（方法二）
+```bash
+cd docker
+docker build --no-cache -t glm4-z1:latest .
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+cd /your_code_path/glm-4-z1_pytorch
+pip install transformers>=4.51.3
+```
+
+### Anaconda（方法三）
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
+```bash
+DTK: 25.04
+python: 3.10
+torch: 2.4.1
+deepspeed: 0.14.2+das.opt2.dtk2504
+```
+`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
+
+其它非深度学习库安装方式如下：
+```bash
+pip install transformers>=4.51.3
+```
+
+## 数据集
+无
+
+## 训练
+### Llama Factory 微调方法(推荐)
+
+1. 训练库安装（**非glm-4-z1_pytorch目录下**），安装版本**大于 v0.9.2**，`Llama-Factory`具体安装方法请参考仓库的README。
+```
+git clone https://developer.sourcefind.cn/codes/OpenDAS/llama-factory
+```
+
+2. 通过[预训练权重](#预训练权重)下载预训练模型，当前用例使用[GLM-Z1-9B-0414](https://huggingface.co/THUDM/GLM-Z1-9B-0414)模型。
+
+#### 全参微调
+
+SFT训练脚本示例，参考`llama-factory/train_full`下对应yaml文件。
+
+**参数修改**：
+- **--model_name_or_path**: 修改为待训练模型地址，如 `/data/GLM-Z1-9B-0414`
+- **--dataset**: 微调训练集名称，可选数据集请参考 `llama-factory/data/dataset_info.json`
+- **--template**: 将 default 修改为 `glm4`
+- **--output_dir**: 模型保存地址
+
+其他参数如：`--learning_rate`、`--save_steps`可根据自身硬件及需求进行修改。
+
+#### lora微调
+
+SFT训练脚本示例，参考`llama-factory/train_lora`下对应yaml文件。
+参数解释同[#全参微调](#全参微调)
+
+## 推理
+### transformers推理方法
+```bash
+## 必须添加HF_ENDPOINT环境变量
+export HF_ENDPOINT=https://hf-mirror.com
+
+python infer_transformers.py --model_path THUDM/GLM-Z1-9B-0414
+```
+
+## result
+<div align=center>
+    <img src="./doc/transformers_results.jpg"/>
+</div>
+
+### 精度
+暂无
+
+## 应用场景
+### 算法类别
+对话问答
+
+### 热点应用行业
+制造,广媒,家居,教育
+
+## 预训练权重
+- [GLM-Z1-9B-0414](https://huggingface.co/THUDM/GLM-Z1-9B-0414)
+- [GLM-Z1-32B-0414](https://huggingface.co/THUDM/GLM-Z1-32B-0414)
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/glm-4-z1_pytorch
+
+## 参考资料
+- https://github.com/THUDM/GLM-4

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
\ No newline at end of file

infer_transformers.py

+import argparse
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_path", type=str, default="THUDM/GLM-Z1-9B-0414")
+    parser.add_argument("--message", type=str, default="Let a, b be positive real numbers such that ab = a + b + 3. Determine the range of possible values for a + b.")
+
+    args = parser.parse_args()
+
+    return args
+
+
+if __name__ == "__main__":
+    # 获取参数信息
+    args = get_args()
+
+    tokenizer = AutoTokenizer.from_pretrained(args.model_path)
+    model = AutoModelForCausalLM.from_pretrained(args.model_path, device_map="auto")
+
+    message = [{"role": "user", "content": args.message}]
+
+    inputs = tokenizer.apply_chat_template(
+        message,
+        return_tensors="pt",
+        add_generation_prompt=True,
+        return_dict=True,
+    ).to(model.device)
+
+    generate_kwargs = {
+        "input_ids": inputs["input_ids"],
+        "attention_mask": inputs["attention_mask"],
+        "max_new_tokens": 4096,
+        "do_sample": False,
+    }
+    out = model.generate(**generate_kwargs)
+    print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

llama-factory/train_full/glm_z1_full_sft.yaml

+### model
+model_name_or_path: THUDM/GLM-Z1-9B-0414
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+deepspeed: examples/deepspeed/ds_z3_config.json  # choices: [ds_z0_config.json, ds_z2_config.json, ds_z3_config.json]
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: glm4
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/glm-z1-9b/full/sft
+logging_steps: 1
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+save_only_model: false
+report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 2
+learning_rate: 1.0e-5
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+resume_from_checkpoint: null
+
+### eval
+# eval_dataset: alpaca_en_demo
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500

llama-factory/train_lora/glm_z1_lora_sft_ds3.yaml

+### model
+model_name_or_path: THUDM/GLM-Z1-9B-0414
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_rank: 8
+lora_target: all
+deepspeed: examples/deepspeed/ds_z3_config.json  # choices: [ds_z0_config.json, ds_z2_config.json, ds_z3_config.json]
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: glm4
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/glm-z1-9b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+save_only_model: false
+report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 2
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+resume_from_checkpoint: null
+
+### eval
+# eval_dataset: alpaca_en_demo
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500

model.properties

+# 模型唯一标识
+modelCode=1500
+# 模型名称
+modelName=glm-4-z1_pytorch
+# 模型描述
+modelDescription=智谱开在数学、代码和逻辑等任务上对模型的进一步强化学习得到GLM4-Z1模型
+# 应用场景
+appScenario=推理,训练,对话问答,制造,广媒,家居,教育
+# 框架类型
+frameType=pytorch