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ChatGLM3-6B finetune

This directory provides fine-tuning examples of the ChatGLM3-6B model, including full fine-tuning and P-Tuning v2. In terms of format, it provides multiple rounds of dialogue fine-tuning samples and input and output format fine-tuning samples.

If the model is downloaded locally, the THUDM/chatglm3-6b field in this article and the code should be replaced with the corresponding address to load the model locally.

Running the example requires python>=3.10. In addition to the basic torch dependency, the example code also requires dependencies to run.

**We provide sample notebook to demonstrate how to use our fine-tuning code. **

pip install -r requirements.txt

Test hardware standards

We only provide single-machine multi-card/multi-machine multi-card running examples, so you will need at least one machine with multiple GPUs. In the default configuration file in this warehouse, we record the usage of video memory:

  • SFT full fine-tuning: evenly distributed among 4 graphics cards, each graphics card occupies 48346MiB of video memory.
  • P-TuningV2 fine-tuning: 1 graphics card, occupying 18426MiB memory.
  • LORA fine-tuning: 1 graphics card, occupying 14082MiB memory.

Please note that this result is for reference only, and the memory usage may be different for different parameters. Please make adjustments based on your hardware conditions.

Multi-turn dialogue format

The multi-round dialogue fine-tuning example adopts the ChatGLM3 dialogue format convention and adds different loss_mask to different characters to calculate loss for multiple rounds of responses in one pass.

For data files, the sample adopts the following format

If you only want to fine-tune your model's conversational capabilities, rather than its tool capabilities, you should organize your data in the following format.

[
  {
    "conversations": [
      {
        "role": "system",
        "content": "<system prompt text>"
      },
      {
        "role": "user",
        "content": "<user prompt text>"
      },
      {
        "role": "assistant",
        "content": "<assistant response text>"
      },
      // ... Muti Turn
      {
        "role": "user",
        "content": "<user prompt text>"
      },
      {
        "role": "assistant",
        "content": "<assistant response text>"
      }
    ]
  }
  // ...
]

**Please note that this method will affect the tool calling function of the model when there are many fine-tuning steps **

If you wish to fine-tune your model's dialog and tool capabilities, you should organize your data in the following format.

[
  {
    "tools": [
      // available tools, format is not restricted
    ],
    "conversations": [
      {
        "role": "system",
        "content": "<system prompt text>"
      },
      {
        "role": "user",
        "content": "<user prompt text>"
      },
      {
        "role": "assistant",
        "content": "<assistant thought to text>"
      },
      {
        "role": "tool",
        "name": "<name of the tool to be called",
        "parameters": {
          "<parameter_name>": "<parameter_value>"
        },
        "observation": "<observation>"
        // don't have to be string
      },
      {
        "role": "assistant",
        "content": "<assistant response to observation>"
      },
      // ... Muti Turn
      {
        "role": "user",
        "content": "<user prompt text>"
      },
      {
        "role": "assistant",
        "content": "<assistant response text>"
      }
    ]
  }
  // ...
]

  • There is no need to manually insert the system prompt about the tool description. The tools field will be used during preprocessing using json.dumps(..., ensure_ascii=False) After formatting, insert it as the first system prompt.

  • Each role can be accompanied by a loss field of type bool, indicating whether the content predicted by this field participates in loss calculate. If there is no such field, the sample implementation does not calculate loss for system and user by default, but calculates loss for other roles.

  • tool is not a native role in ChatGLM3. The tool here will be automatically converted into an assistant with tool call metadata during the preprocessing stage. role (default loss is calculated) and an observation role representing the tool return value (loss is not calculated).

  • The fine-tuning task of Code interpreter has not been implemented yet.

  • The system role is optional, but if the system role exists, it must appear in user Before the character, the system character can only appear once in a complete dialogue data (regardless of single round or multiple rounds of dialogue).

Dataset format example

Here we take the AdvertiseGen data set as an example, You can download it from Google Drive Or Tsinghua Cloud download the AdvertiseGen data set. Place the decompressed AdvertiseGen directory in the data directory and convert it into the following format data set yourself.

Please note that the verification set is added to the current fine-tuning code. Therefore, for a complete set of fine-tuning data sets, the training data set and the verification data set must be included, and the test data set does not need to be filled in. Or directly use the validation data set instead.

{"conversations": [{"role": "user", "content": "Type#skirt*skirt length#skirt"}, {"role": "assistant", "content": "This is versatile Fashionable fairy skirt, the overall design is very elegant and casual. Every girl can instantly turn into a fairy after wearing it. The material is very light and breathable, making it very comfortable to wear in summer."} ]}

Configuration file

Fine-tuning configuration files are located in the config directory and include the following files:

  1. ds_zereo_2 / ds_zereo_3.json: deepspeed configuration file.
  2. lora.yaml / ptuning.yaml / sft.yaml: Configuration files for different models, including model parameters, optimizer parameters, training parameters, etc. Some important parameters are explained as follows:
    • data_config section
      • train_file: The file path of the training data set.
      • val_file: The file path of the verification data set.
      • test_file: The file path of the test data set.
      • num_proc: Number of processes used when loading data.
    • max_input_length: The maximum length of the input sequence.
    • max_output_length: The maximum length of the output sequence.
    • training_args section
      • output_dir: Directory for saving models and other outputs.
      • max_steps: The maximum number of steps for training.
      • per_device_train_batch_size: training batch size per device (e.g. GPU).
      • dataloader_num_workers: The number of worker threads used when loading data.
      • remove_unused_columns: Whether to remove unused columns in the data.
      • save_strategy: model saving strategy (for example, how many steps should be saved).
      • save_steps: How many steps should be taken to save the model.
      • log_level: log level (such as info).
      • logging_strategy: logging strategy.
      • logging_steps: How many steps to log.
      • per_device_eval_batch_size: Evaluation batch size per device.
      • evaluation_strategy: Evaluation strategy (e.g. how many steps should be evaluated).
      • eval_steps: How many steps to evaluate.
      • predict_with_generate: Whether to use generate mode for prediction.
    • generation_config section
      • max_new_tokens: The maximum number of new tokens generated.
    • peft_config section
      • peft_type: The parameter valid adjustment type used (e.g. LORA).
      • task_type: task type, here is the causal language model (CAUSAL_LM).
    • Lora parameters:
      • r: LoRA rank.
      • lora_alpha: Scaling factor for LoRA.
      • lora_dropout: dropout probability used in LoRA layer
    • P-TuningV2 parameters:
      • num_virtual_tokens: The number of virtual tokens.

Start fine-tuning

Use the following code to execute single machine multiple cards/multiple machines multiple cards operation.

cd finetune_demo
OMP_NUM_THREADS=1 torchrun --standalone --nnodes=1 --nproc_per_node=8  finetune_hf.py  data/AdvertiseGen/  THUDM/chatglm3-6b  configs/lora.yaml configs/ds_zero_2.json

Execute Single machine single card operation through the following code.

cd finetune_demo
python finetune_hf.py data/AdvertiseGen/ THUDM/chatglm3-6b configs/lora.yaml

Fine-tuning from a checkpoint

If you train according to the above method, each fine-tuning will start from scratch. If you want to fine-tune from a half-trained model, you can add a fourth parameter, which has two ways to pass in:

  1. yes, automatically start training from the last saved Checkpoint
  2. XX, breakpoint number, for example, 600 means training from Checkpoint number 600

For example, this is an example of continuing fine-tuning from the last saved point


cd finetune_demo
python finetune_hf.py  data/AdvertiseGen/  THUDM/chatglm3-6b  configs/lora.yaml yes

Use the fine-tuned model

Verify the fine-tuned model in inference_hf.py

You can use our fine-tuned model in finetune_demo/inference_hf.py, which can be easily tested with just one line of code.

python inference_hf.py your_finetune_path --prompt your prompt

In this way, the answer you get is a fine-tuned answer.

Use the fine-tuned model in other demos in this repos or external repos

You can use our lora and fully parameterized fine-tuned models in any demo, as follows:

  1. Use the method of reading the model in finetune_demo/inference_hf.py to replace the method of reading the model in the demo.

Please note that for LORA and P-TuningV2 we do not merge the trained models, but in adapter_config.json The fine-tuning path is recorded in . If your original model location changes, you should modify the path of base_model_name_or_path in adapter_config.json.

Please note that we have only tested using NVIDIA Hopper (representative GPU: H100) and Ampère (representative GPU: A100) architecture and series of graphics cards. If you use a graphics card with another architecture, you may experience

  1. Unknown training problem/Video memory usage is different from the above.
  2. The architecture is too low and does not support certain features.
  3. The problem of reasoning effect. > The above three situations are problems that the community has encountered before. Although the probability is extremely low, if you encounter the above problems, you can try to solve them in the community.
def load_model_and_tokenizer(
        model_dir: Union[str, Path], trust_remote_code: bool = True
) -> tuple[ModelType, TokenizerType]:
    model_dir = _resolve_path(model_dir)
    if (model_dir / 'adapter_config.json').exists():
        model = AutoPeftModelForCausalLM.from_pretrained(
            model_dir, trust_remote_code=trust_remote_code, device_map='auto'
        )
        tokenizer_dir = model.peft_config['default'].base_model_name_or_path
    else:
        model = AutoModelForCausalLM.from_pretrained(
            model_dir, trust_remote_code=trust_remote_code, device_map='auto'
        )
        tokenizer_dir = model_dir
    tokenizer = AutoTokenizer.from_pretrained(
        tokenizer_dir, trust_remote_code=trust_remote_code
    )
    return model, tokenizer
  1. Read the fine-tuned model, please note that you should use the location of the fine-tuned model, for example, if your model location is /path/to/finetune_adapter_model , the original model address is path/to/base_model, then you should use /path/to/finetune_adapter_model as model_dir.
  2. After completing the above operations, you can use the fine-tuned model normally, and other calling methods remain unchanged.

hint

  1. Before starting training, the fine-tuning code will print the preprocessing information of the first training data ( it is commented by default and can be uncommented), which is displayed as
Sanity
Check >> >> >> >> >> >> >
'[gMASK]': 64790 ->   -100
'sop': 64792 ->   -100
'<|system|>': 64794 ->   -100
'': 30910 ->   -100
'\n': 13 ->   -100
'Answer': 20115 ->   -100
'the': 267 ->   -100
'following': 1762 ->   -100
...
'know': 683 ->   -100
'the': 267 ->   -100
'response': 3010 ->   -100
'details': 3296 ->   -100
'.': 30930 ->   -100
'<|assistant|>': 64796 ->   -100
'': 30910 ->  30910
'\n': 13 ->     13
'I': 307 ->    307
'need': 720 ->    720
'to': 289 ->    289
'use': 792 ->    792
...
<< << << << << << < Sanity
Check

words, each line represents a detokenized string, token_id and target_id in turn. Among them, target_id is the index of token_id in the model vocabulary, and -100 means that Token does not participate in loss calculation.

  1. The function of _prepare_model_for_training is to iterate through all the trainable parameters of the model and ensure that their data type is torch.float32. This is necessary in some cases because mixed precision training or other operations may change the data type of the model parameters. This code is opened by default and can be commented, but if you use If there is a problem with half format training, you can switch back to this code, and the video memory may increase.
  2. In our Huggingface model code, there is the following content: 
    if self.gradient_checkpointing and self.training:
            layer_ret = torch.utils.checkpoint.checkpoint(
                layer,
                hidden_states,
                attention_mask,
                rotary_pos_emb,
                kv_caches[index],
                use_cache,
                use_reentrant=False
            )
    This may cause the video memory to increase during training, so if you have insufficient video memory, you can try changing use_reentrant to True.
  3. The fine-tuned model can use any model acceleration framework that supports peft loading. Here, we do not provide a demo.
  4. There are certain differences between the fine-tuning data set format of this warehouse and the API fine-tuning data set format.
    • The messages field in the ZhipuAI API fine-tuning data set is the conversation field in this warehouse.
    • The fine-tuning file in ZhipuAI API is jsonl. In this warehouse, you need to simply change the file name to json.

Citation


@inproceedings{liu2022p,
title={P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks},
author={Liu, Xiao and Ji, Kaixuan and Fu, Yicheng and Tam, Weng and Du, Zhengxiao and Yang, Zhilin and Tang, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short
Papers)},
pages={61--68},
year={2022}
}

@misc{tang2023toolalpaca,
title={ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases},
author={Qiaoyu Tang and Ziliang Deng and Hongyu Lin and Xianpei Han and Qiao Liang and Le Sun},
year={2023},
eprint={2306.05301},
archivePrefix={arXiv},
primaryClass={cs.CL}
}