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Model: m-a-p/MuPT-v0-8192-1.97B
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
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- music
+- art
+---
+
+<div align="center">
+    <img src="Yi_logo.svg" width="150px" style="display: inline-block;">
+    <img src="m-a-p.png" width="150px" style="display: inline-block;">
+</div>
+
+## SMuPT: Symbolic Music Generative Pre-trained Transformer
+
+SMuPT is a series of pre-trained models for symbolic music generation. It was trained on a large-scale dataset of symbolic music, including millions of monophonic and polyphonic pieces from different genres and styles. The models are trained with the LLama2 architecture, and can be further used for downstream music generation tasks such as melody generation, accompaniment generation, and multi-track music generation. 
+
+- 09/01/2024: a series of pre-trained SMuPT models are released, with parameters ranging from 110M to 1.3B.
+
+## Model architecture
+
+The details of model architecture of SMuPT-v0 are listed below:
+
+| Name | Parameters | Training Data(Music Pieces) | Seq Length | Hidden Size | Layers | Heads |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
+| SMuPT-v0-8192-110M | 110M | 7M x 5.8 epochs | 8192 | 768 | 12 | 12 |
+| SMuPT-v0-8192-345M | 345M | 7M x 4 epochs | 8192 | 1024 | 24 | 16 |
+| SMuPT-v0-8192-770M | 770M | 7M x 3 epochs | 8192 | 1280 | 36 | 20 |
+| SMuPT-v0-8192-1.3B | 1.3B | 7M x 2.2 epochs | 8192 | 1536 | 48 | 24 |
+
+## Model Usage
+
+There are several ways to use our pre-trained SMuPT models, we now the usage based on [Megatron-LM](https://github.com/NVIDIA/Megatron-LM/tree/main). Huggingface format will be supported soon.
+
+Before starting, make sure you have setup the relevant environment and codebase. 
+ 
+```shell
+# pull Megatron-LM codebase
+mkdir -p /path/to/workspace && cd /path/to/workspace
+git clone https://github.com/NVIDIA/Megatron-LM.git
+
+# download the pre-trained SMuPT models checkpoint and vocab files from Huggingface page
+mkdir -p /models/SMuPT_v0_8192_1.3B && cd /models/SMuPT_v0_8192_1.3B
+wget -O model_optim_rng.pt https://huggingface.co/m-a-p/SMuPT_v0_8192_1.3B/resolve/main/model_optim_rng.pt?download=true
+wget -O newline.vocab https://huggingface.co/m-a-p/SMuPT_v0_8192_1.3B/resolve/main/newline.vocab?download=true
+wget -O newline.txt https://huggingface.co/m-a-p/SMuPT_v0_8192_1.3B/resolve/main/newline.txt?download=true
+```
+
+We recommend using the latest version of [NGC's PyTorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) for SMuPT inference. See more details in [Megatron-LM](https://github.com/NVIDIA/Megatron-LM/tree/main)
+
+```shell
+# pull the latest NGC's PyTorch container, mount the workspace directory and enter the container
+docker run --gpus all -it --name megatron --shm-size=16g -v $PWD:/workspace -p 5000:5000 nvcr.io/nvidia/pytorch:23.11-py3 /bin/bash
+```
+
+Once you enter the container, you can start a REST server for inference. 
+
+<details>
+    <summary>Click to expand the example script</summary>
+
+    #!/bin/bash
+    # This example will start serving the 1.3B model.
+    export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+    DISTRIBUTED_ARGS="--nproc_per_node 1 \
+                    --nnodes 1 \
+                    --node_rank 0 \
+                    --master_addr localhost \
+                    --master_port 6000"
+
+    CHECKPOINT=/path/to/model/checkpoint/folder
+    VOCAB_FILE=/path/to/vocab/file
+    MERGE_FILE=/path/to/merge/file
+
+    MODEL_SIZE="1.3B"
+    if   [[ ${MODEL_SIZE} == "110M" ]];   then HIDDEN_SIZE=768;  NUM_HEAD=12; NUM_QUERY_GROUP=12; NUM_LAYERS=12; FFN_HIDDEN_SIZE=3072; NORM_EPS=1e-5;
+    elif [[ ${MODEL_SIZE} == "345M" ]];   then HIDDEN_SIZE=1024;  NUM_HEAD=16; NUM_QUERY_GROUP=16; NUM_LAYERS=24; FFN_HIDDEN_SIZE=4096; NORM_EPS=1e-5;
+    elif [[ ${MODEL_SIZE} == "770M" ]];   then HIDDEN_SIZE=1280;  NUM_HEAD=20; NUM_QUERY_GROUP=20; NUM_LAYERS=36; FFN_HIDDEN_SIZE=5120; NORM_EPS=1e-5;
+    elif [[ ${MODEL_SIZE} == "1.3B" ]];   then HIDDEN_SIZE=1536;  NUM_HEAD=24; NUM_QUERY_GROUP=24; NUM_LAYERS=48; FFN_HIDDEN_SIZE=6144; NORM_EPS=1e-5;
+    else echo "invalid MODEL_SIZE: ${MODEL_SIZE}"; exit 1
+    fi
+    MAX_SEQ_LEN=8192
+    MAX_POSITION_EMBEDDINGS=8192
+
+    pip install flask-restful
+
+    torchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py   \
+        --tensor-model-parallel-size 1  \
+        --pipeline-model-parallel-size 1  \
+        --num-layers ${NUM_LAYERS}  \
+        --hidden-size ${HIDDEN_SIZE}  \
+        --ffn-hidden-size ${FFN_HIDDEN_SIZE} \
+        --load ${CHECKPOINT}  \
+        --group-query-attention \
+        --num-query-groups ${NUM_QUERY_GROUP} \
+        --position-embedding-type rope \
+        --num-attention-heads ${NUM_HEAD}  \
+        --max-position-embeddings ${MAX_POSITION_EMBEDDINGS}  \
+        --tokenizer-type GPT2BPETokenizer  \
+        --normalization RMSNorm \
+        --norm-epsilon ${NORM_EPS} \
+        --make-vocab-size-divisible-by 1 \
+        --swiglu \
+        --use-flash-attn \
+        --bf16  \
+        --micro-batch-size 1  \
+        --disable-bias-linear \
+        --no-bias-gelu-fusion \
+        --untie-embeddings-and-output-weights \
+        --seq-length ${MAX_SEQ_LEN}  \
+        --vocab-file $VOCAB_FILE  \
+        --merge-file $MERGE_FILE  \
+        --attention-dropout 0.0 \
+        --hidden-dropout 0.0 \
+        --weight-decay 1e-1 \
+        --clip-grad 1.0 \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --adam-eps 1e-8 \
+        --seed 42
+
+</details>
+
+
+Use CURL to query the server directly, note that the newline token `\n` is represented by `<n>` in the vocabulary, so we need to replace the newline token with `<n>` in both the prompt and the generated tokens. 
+
+```shell
+curl 'http://localhost:6000/api' -X 'PUT' -H 'Content-Type: application/json; charset=UTF-8'  -d '{"prompts":["X:1<n>L:1/8<n>Q:1/8=200<n>M:4/4<n>K:Gmin<n>|:\"Gm\" BGdB"], "tokens_to_generate":4096}'
+```
+Processed Output:
+```shell
+X:1
+L:1/8
+Q:1/8=200
+M:4/4<n>K:Gmin
+|:\"Gm\" BGdB fdBG |\"F\" AFcF dFcF |\"Gm\" BGdG gFBF |\"F\" AFAG AF F2 |\"Gm\" BGBd fffd |\"F\" cdcB cdeg |
+\"Gm\" fdcB\"Eb\" AFcA |1 BGFG\"F\" AFGc :|2 BGFG\"F\" AF F2 ||
+```
+
+Once you encode the generated tokens into audio, you will hear the following music.
+
+<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/640701cb4dc5f2846c91d4eb/gnBULaFjcUyXYzzIwXLZq.mpga"></audio>

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+</svg>

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "eos_token_id": 3,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 8192,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 24,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "tokenizer_class": "MuPTTokenizer",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "vocab_size": 50000
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "eos_token_id": 3,
+  "pad_token_id": 0,
+  "transformers_version": "4.36.2"
+}

model_optim_rng.pt

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pytorch_model.bin

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tokenization_mupt.py

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+# coding=utf-8
+# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for OpenAI GPT."""
+
+
+import json
+import os
+from functools import lru_cache
+from typing import List, Optional, Tuple
+
+import regex as re
+
+from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {
+    "vocab_file": "vocab.json",
+    "merges_file": "merges.txt",
+}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
+        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
+        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
+        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
+        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
+    },
+    "merges_file": {
+        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
+        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
+        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
+        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
+        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
+    },
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "mupt-110M": 8192,
+    "mupt-345M": 8192,
+    "mupt-770M": 8192,
+    "mupt-1.3B": 8192,
+}
+
+
+@lru_cache()
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
+    characters the bpe code barfs on.
+
+    The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
+    if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
+    decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
+    tables between utf-8 bytes and unicode strings.
+    """
+    bs = (
+        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+    """
+    Return set of symbol pairs in a word.
+
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+class MuPTTokenizer(PreTrainedTokenizer):
+    """
+    Construct a GPT-2 tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
+    be encoded differently whether it is at the beginning of the sentence (without space) or not:
+
+    ```python
+    >>> from transformers import GPT2Tokenizer
+
+    >>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+    >>> tokenizer("Hello world")["input_ids"]
+    [15496, 995]
+
+    >>> tokenizer(" Hello world")["input_ids"]
+    [18435, 995]
+    ```
+
+    You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
+    call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
+
+    <Tip>
+
+    When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
+
+    </Tip>
+
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        merges_file (`str`):
+            Path to the merges file.
+        errors (`str`, *optional*, defaults to `"replace"`):
+            Paradigm to follow when decoding bytes to UTF-8. See
+            [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+        unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
+            The beginning of sequence token.
+        eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
+            The end of sequence token.
+        pad_token (`str`, *optional*):
+            The token used for padding, for example when batching sequences of different lengths.
+        add_prefix_space (`bool`, *optional*, defaults to `False`):
+            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+            other word. (GPT2 tokenizer detect beginning of words by the preceding space).
+        add_bos_token (`bool`, *optional*, defaults to `False`):
+            Whether or not to add an initial beginning of sentence token to the input. This allows to treat the leading
+            word just as any other word.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        merges_file,
+        errors="replace",
+        unk_token="<unk>",
+        bos_token="<bos>",
+        eos_token="<eos>",
+        pad_token="<pad>",
+        add_prefix_space=False,
+        add_bos_token=False,
+        **kwargs,
+    ):
+        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+
+        self.add_bos_token = add_bos_token
+
+        with open(vocab_file, encoding="utf-8") as vocab_handle:
+            self.encoder = json.load(vocab_handle)
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.errors = errors  # how to handle errors in decoding
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        with open(merges_file, encoding="utf-8") as merges_handle:
+            bpe_merges = merges_handle.read().split("\n")[1:-1]
+        bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
+        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+        self.cache = {}
+        self.add_prefix_space = add_prefix_space
+
+        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
+        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
+
+        super().__init__(
+            errors=errors,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            add_prefix_space=add_prefix_space,
+            add_bos_token=add_bos_token,
+            **kwargs,
+        )
+
+    @property
+    def vocab_size(self):
+        return len(self.encoder)
+
+    def get_vocab(self):
+        return dict(self.encoder, **self.added_tokens_encoder)
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token
+
+        while True:
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                except ValueError:
+                    new_word.extend(word[i:])
+                    break
+                else:
+                    new_word.extend(word[i:j])
+                    i = j
+
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = " ".join(word)
+        self.cache[token] = word
+        return word
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is None:
+            return output
+
+        return output + bos_token_ids + token_ids_1
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if not self.add_bos_token:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0))
+        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
+
+    def _tokenize(self, text):
+        """Tokenize a string."""
+        bpe_tokens = []
+        for token in re.findall(self.pat, text):
+            token = "".join(
+                self.byte_encoder[b] for b in token.encode("utf-8")
+            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
+            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
+        return bpe_tokens
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.decoder.get(index)
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        text = "".join(tokens)
+        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
+        return text
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+        merge_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
+        )
+
+        with open(vocab_file, "w", encoding="utf-8") as f:
+            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+        index = 0
+        with open(merge_file, "w", encoding="utf-8") as writer:
+            writer.write("#version: 0.2\n")
+            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
+                        " Please check that the tokenizer is not corrupted!"
+                    )
+                    index = token_index
+                writer.write(" ".join(bpe_tokens) + "\n")
+                index += 1
+
+        return vocab_file, merge_file
+
+    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
+        add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
+        if is_split_into_words or add_prefix_space:
+            text = " " + text
+        return (text, kwargs)
+
+    @property
+    def default_chat_template(self):
+        """
+        A simple chat template that ignores role information and just concatenates messages with EOS tokens.
+        """
+        logger.warning_once(
+            "\nNo chat template is defined for this tokenizer - using the default template "
+            f"for the {self.__class__.__name__} class. If the default is not appropriate for "
+            "your model, please set `tokenizer.chat_template` to an appropriate template. "
+            "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
+        )
+        return "{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}"

tokenizer_config.json

@@ -0,0 +1,10 @@
+{
+    "model_max_length": 8192,
+    "tokenizer_class": "MuPTTokenizer",
+    "auto_map": {
+      "AutoTokenizer": [
+        "tokenization_mupt.MuPTTokenizer",
+        null
+        ]
+    }
+  }