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Mistral-finetune

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mistral-finetune is a light-weight codebase that enables memory-efficient and performant finetuning of Mistral's models. It is based on LoRA, a training paradigm where most weights are frozen and only 1-2% of additional weights in the form of low-rank matrix perturbations are trained.

For maximum efficiency it is recommended to use an A100 or H100 GPU. The codebase is optimized for multi-GPU-single-node training setups, but for smaller models, such as the 7B a single GPU suffices.

Note

News

Installation

To get started with Mistral LoRA fine-tuning, follow these steps:

  1. Clone this repository:
cd $HOME && git clone https://github.com/mistralai/mistral-finetune.git
  1. Install all required dependencies:
cd mistral-finetune
pip install -r requirements.txt

Model download

We recommend fine-tuning one of the official Mistral models which you can download here:

ModelLinkChecksum
7B Base V37B Base0663b293810d7571dad25dae2f2a5806
7B Instruct v37B Instruct v380b71fcb6416085bcb4efad86dfb4d52
8x7B Base V18x7B Base(HF link)
8x7B Instruct V18x7B Instruct8e2d3930145dc43d3084396f49d38a3f
8x22 Instruct V38x22 Instruct471a02a6902706a2f1e44a693813855b
8x22B Base V38x22B Basea2fa75117174f87d1197e3a4eb50371a
12B Instruct12B Instruct (Mistral-Nemo)296fbdf911cb88e6f0be74cd04827fe7
12B Base12 Base (Mistral-Nemo)c5d079ac4b55fc1ae35f51f0a3c0eb83
Mistral Large 2123B Instruct (Large v2)fc602155f9e39151fba81fcaab2fa7c4

Important Notice: For 8x7B Base V1 and 8x7B Instruct V1, it is necessary to use our v3 tokenizer and extend the vocabulary size to 32768 prior to fine-tuning. For detailed instructions on this process, please refer to the "Model extension" section.

E.g., to download the 7B-base model you can run the following command:

mkdir -p ~/${HOME}/mistral_models
cd ${HOME} && wget https://models.mistralcdn.com/mistral-7b-v0-3/mistral-7B-v0.3.tar
tar -xf mistral-7B-v0.3.tar -C mistral_models

Make sure to modify your training script and add the path to the downloaded folder as model_id_or_path.

E.g., modify example/7B.yaml to include the absolute path to $HOME/mistral_models/7B:

model_id_or_path: "/Users/johndoe/mistral_models/7B"

Prepare dataset

To ensure effective training, mistral-finetune has strict requirements for how the training data has to be formatted.

All data files must be stored in jsonl format files.

You can build two types of data files:

Pretrain:

Pretrain data corresponds to plain text data stored in the "text" key. E.g:

{"text": "Text contained in document n°1"}
{"text": "Text contained in document n°2"}

Instruct:

Currently two different types of instruction following data are supported:

{
  "messages": [
    {
      "role": "user",
      "content": "User interaction n°1 contained in document n°1"
    },
    {
      "role": "assistant",
      "content": "Bot interaction n°1 contained in document n°1"
    },
    {
      "role": "user",
      "content": "User interaction n°2 contained in document n°1"
    },
    {
      "role": "assistant",
      "content": "Bot interaction n°2 contained in document n°1"
    }
  ]
}
{
  "messages": [
    {
      "role": "user",
      "content": "User interaction n°1 contained in document n°2"
    },
    {
      "role": "assistant",
      "content": "Bot interaction n°1 contained in document n°2"
    },
    {
      "role": "user",
      "content": "User interaction n°2 contained in document n°2"
    },
    {
      "role": "assistant",
      "content": "Bot interaction n°2 contained in document n°2",
      "weight": 0,  # don't train on n°2
    },
    {
      "role": "user",
      "content": "User interaction n°3 contained in document n°2"
    },
    {
      "role": "assistant",
      "content": "Bot interaction n°3 contained in document n°2"
    }
  ]
}

Note: In function calling the "id" of "tool_calls" and the "tool_call_id" are randomly generated strings of exactly 9 chars. We recommend to generate this automatically in a data preparation script as is done here.

E.g.:

{
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant who has access to the following functions to help the user, you can use the functions if needed"
    },
    {
      "role": "user",
      "content": "Can you help me generate an anagram of the word \"listen\"?"
    },
    {
      "role": "assistant",
      "tool_calls": [
        {
          "id": "TX92Jm8Zi",
          "type": "function",
          "function": {
            "name": "generate_anagram",
            "arguments": "{\"word\": \"listen\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"anagram\": \"silent\"}",
      "tool_call_id": "TX92Jm8Zi"
    },
    {
      "role": "assistant",
      "content": "The anagram of the word \"listen\" is \"silent\"."
    },
    {
      "role": "user",
      "content": "That's amazing! Can you generate an anagram for the word \"race\"?"
    },
    {
      "role": "assistant",
      "tool_calls": [
        {
          "id": "3XhQnxLsT",
          "type": "function",
          "function": {
            "name": "generate_anagram",
            "arguments": "{\"word\": \"race\"}"
          }
        }
      ]
    }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "generate_anagram",
        "description": "Generate an anagram of a given word",
        "parameters": {
          "type": "object",
          "properties": {
            "word": {
              "type": "string",
              "description": "The word to generate an anagram of"
            }
          },
          "required": [
            "word"
          ]
        }
      }
    }
  ]
}

Verify dataset

Before starting a training run you should verify that your dataset is correctly formatted and get an estimation of the training time. You can do so by using the ./utils/validate_data script.

Note that this step is crucial to ensure that the data is correctly formatted.

Instruction following

Let's go over a simple example to train a model in instruction following:

Create the data folder and navigate to the folder.

cd $HOME && mkdir -p data && cd $HOME/data

Load the data into a Pandas Dataframe.

Note: Make sure to have pandas and pyarrow installed (pip install pandas pyarrow).

import pandas as pd

df = pd.read_parquet('https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k/resolve/main/data/test_gen-00000-of-00001-3d4cd8309148a71f.parquet')

df_train=df.sample(frac=0.95,random_state=200)
df_eval=df.drop(df_train.index)

df_train.to_json("ultrachat_chunk_train.jsonl", orient="records", lines=True)
df_eval.to_json("ultrachat_chunk_eval.jsonl", orient="records", lines=True)

Modify example/7B.yaml to include the absolute path to $HOME/data/ultrachat_chunk_train.jsonl as well as a dataset mixing weight for training and $HOME/data/ultrachat_chunk_eval.jsonl for eval, e.g.

data:
  instruct_data: "/Users/johndoe/data/ultrachat_chunk_train.jsonl"
  eval_instruct_data: "/Users/johndoe/data/ultrachat_chunk_eval.jsonl"

Now you can verify your training yaml to make sure the data is correctly formatted and to get an estimate of your training time.

cd $HOME/mistral-finetune
python -m utils.validate_data --train_yaml example/7B.yaml

Upon completion you should see an error report with many of the following errors:

The data in line 1412 of dataset /Users/johndoe/data/ultrachat_chunk_eval.jsonl is incorrectly formatted. Expected last role to be one of: [assistant] but got user
The data in line 1413 of dataset /Users/johndoe/data/ultrachat_chunk_eval.jsonl is incorrectly formatted. Expected last role to be one of: [assistant] but got user
The data in line 1414 of dataset /Users/johndoe/data/ultrachat_chunk_eval.jsonl is incorrectly formatted. Expected last role to be one of: [assistant] but got user
The data in line 1415 of dataset /Users/johndoe/data/ultrachat_chunk_eval.jsonl is incorrectly formatted. Expected last role to be one of: [assistant] but got user

Many conversations seem to end with the 'user' role which is unnecessary as we only train on 'assistant' messages and thus would unnecessarily process data.

You can make use of ./utils/reformat_data.py to correct the data:

cd $HOME/mistral-finetune
python -m utils.reformat_data $HOME/data/ultrachat_chunk_train.jsonl
python -m utils.reformat_data $HOME/data/ultrachat_chunk_eval.jsonl

You should see that a couple of samples will be skipped.

Upon correction of the dataset, run the script again

cd $HOME/mistral-finetune
python -m utils.validate_data --train_yaml example/7B.yaml

You should get a summary of the data input and training parameters:

Train States
 --------------------
{
   "expected": {
       "eta": "00:52:44",
       "data_tokens": 25169147,
       "train_tokens": 131072000,
       "epochs": "5.21",
       "max_steps": 500,
       "data_tokens_per_dataset": {
           "/Users/johndoe/data/ultrachat_chunk_train.jsonl": "25169147.0"
       },
       "train_tokens_per_dataset": {
           "/Users/johndoe/data/ultrachat_chunk_train.jsonl": "131072000.0"
       },
       "epochs_per_dataset": {
           "/Users/johndoe/data/ultrachat_chunk_train.jsonl": "5.2"
       }
   },
}

Having max_steps set to 500 would lead to iterating through the dataset roughly 5 times which is reasonable, but might be a bit too much. A recommended setting is shown below which would only take 30min on a 8xH100 cluster.

Function calling

Next let's go over a more advanced use case to fine-tune a model on function calling. Function calling requires the data to be in the format as explained above. Let's go over an example.

Create the data folder and navigate to the folder.

cd $HOME && mkdir -p data && cd $HOME/data

Load the data into a Pandas Dataframe.

Note: Make sure to have pandas and pyarrow installed (pip install pandas pyarrow).

import pandas as pd

df = pd.read_parquet('https://huggingface.co/datasets/Locutusque/function-calling-chatml/resolve/main/data/train-00000-of-00001-f0b56c6983b4a78f.parquet')

df_train=df.sample(frac=0.95,random_state=200)
df_eval=df.drop(df_train.index)

df_train.to_json("glaive_train.jsonl", orient="records", lines=True)
df_eval.to_json("glaive_eval.jsonl", orient="records", lines=True)

As one can see the dataset does not follow the required function calling format, so it will need to be reformatted. Among other things "from" should be renamed to "user" and superfluous "\n" characters should be removed. For this dataset you can make use of ./utils/reformat_data_glaive.py:

cd $HOME/mistral-finetune
python -m utils.reformat_data_glaive $HOME/data/glaive_train.jsonl
python -m utils.reformat_data_glaive $HOME/data/glaive_eval.jsonl

Running this command will make sure that most samples are in the correct format.

Note: It is impossible to write reformatting scripts that work for all kinds of datasets. If you have datasets that don't yet follow the required format above, you will most probably have to create a reformatting script yourself (mistral-chat or chat-gpt is your best friend here!).

You can now validate the dataset by setting data.instruct_data and data.eval_instruct_data to $HOME/data/glaive_train.jsonl and $HOME/data/glaive_eval.jsonl in example/7B.yaml respectively.

The reformatted datasets still have some errors which can be removed with --create_corrected. For this, make sure to add --create_corrected as follows:

cd $HOME/mistral-finetune
python -m utils.validate_data --train_yaml example/7B.yaml --create_corrected

Running this command will show a couple of errors and save two new datasets $HOME/data/glaive_train.jsonl.corrected and $HOME/data/glaive_eval.jsonl.corrected. Make sure to use these two dataset in example/7B.yaml and run the command again. Now the dataset should be correctly formatted!

Start training

Having followed the dataset verification section, we can now start training. For faster training, we recommend setting max_steps to only 300. Make sure to define run_dir to your experiment folder and optionally set wandb_project to a Weights & Biases project for logging`, e.g.:

max_steps: 300
run_dir: "/Users/johndoe/ultra_chat_test"
wandb.project: ultra_chat

Optionally you can also set wandb

Save the training configuration and start training! Make sure to set --nproc-per-node to the number of available GPUs.

cd $HOME/mistral-finetune
torchrun --nproc-per-node 8 --master_port $RANDOM -m train example/7B.yaml

Training on ultra-chat should take around 30min on a 8xH100 node and the resulting weights should give an MT Bench score around 6.3.

Training on glaive should take around 1h on a 8xH100 node and the resulting weights should work nicely for function calling.

Customizing training configuration

The example mistral-finetune/examples/7B defines reasonable parameters for learning rate, weight decay, etc... but you are advised to customize these settings for your use case.

Generally, a training configuration should fill the following parameters:

Inference

Once your model is trained, you should try it out in inference. We recommend using mistral-inference.

Make sure to have mistral_inference correctly installed:

pip install mistral_inference

Assuming your lora.safetensors is saved under $HOME/ultra_chat_test/checkpoints/checkpoint_000300/consolidated/lora.safetensors, you can chat with the model using mistral_inference, e.g.:

mistral-chat /mnt/slow/runs/patrick/mistral-finetune/7B/ --max_tokens 256 --temperature 1.0 --instruct --lora_path $HOME/ultra_chat_test/checkpoints/checkpoint_000300/consolidated/lora.safetensors

Adding Weights and Biases (wandb) Support

We have added explicit support for Weights and Biases to help you monitor and visualize your training runs. This integration allows you to log various metrics and track experiments easily.

Setting Up Weights and Biases

To use Weights and Biases with mistral-finetune, follow these steps:

  1. Install Weights and Biases:

    Make sure you have the wandb library installed. You can install it using pip:

   pip install wandb

Viewing Your Logs

Once the training starts, you can monitor the progress in real-time by visiting your wandb project dashboard. All metrics, including training loss, evaluation loss, learning rate, etc., will be logged and visualized.

For more details on how to use wandb, visit the Weights and Biases documentation.

Model extension

Important: Note that one can only fine-tune mistral models that are compatible with the v3 tokenizer which entails that the models have a vocabulary size of 32768 - not 32000. One can however easily extend older version of vocabulary size 32000 to have a vocabulary size of 32768 by using:

python -m utils.extend_model_vocab --original_model_ckpt /folder/to/old/model --extended_model_ckpt /folder/to/extended/model

Once the extension has worked, one can fine-tune using the newly created model checkpoint in /folder/to/extended/model.

FAQ:

We see a higher degree of performance variance in when fine-tuning MoE models. It's not unusual to find that fine-tuning MoE models with different seeds can lead to a high variance in performance. We did not observe such a high variance with dense models. Therefore, we suggest running multiple instances of the same fine-tuning process on MoEs models and selecting the one that performs best.

You can use the following script to find out: https://github.com/mistralai/mistral-finetune/blob/main/utils/validate_data.py. This script accepts a .yaml training file as input and returns the number of tokens the model is being trained on.

One possible solution is to reduce the batch size per GPU. The batch size is equal to seq_len x batch_size. Try setting batch_size to 1 and reduce seq_len. You can define the batch_size and seq_len in the .yaml file.