Upload folder using huggingface_hub

LICENSE.txt

@@ -0,0 +1,51 @@
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
+"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
+
+"Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.
+
+"Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.
+
+"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
+
+"Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.
+
+"Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.
+
+"Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).
+
+"Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.
+
+"Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
+
+"Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.
+
+2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.
+
+3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.
+
+4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:
+
+You must give any other recipients of the Work or Derivative Works a copy of this License; and
+You must cause any modified files to carry prominent notices stating that You changed the files; and
+You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and
+If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License.
+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.
+
+5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
+
+6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.
+
+7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.
+
+8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+
+9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+
+END OF TERMS AND CONDITIONS

config.json

@@ -0,0 +1,43 @@
+{
+  "architectures": [
+    "ZhinaoForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_zhinao.ZhinaoConfig",
+    "AutoModelForCausalLM": "modeling_zhinao.ZhinaoForCausalLM"
+  },
+  "bf16": true,
+  "fp16": false,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.01,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_max_length": 4096,
+  "model_type": "zhinao",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "quantization_config": {
+    "bits": 4,
+    "checkpoint_format": "gptq",
+    "damp_percent": 0.01,
+    "desc_act": false,
+    "group_size": 128,
+    "model_file_base_name": "gptq_model-4bit-128g",
+    "model_name_or_path": "360Zhinao-7B-Chat-4K-Int4",
+    "quant_method": "gptq",
+    "static_groups": false,
+    "sym": true,
+    "true_sequential": true
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.2",
+  "use_cache": false,
+  "use_flash_attn": "auto",
+  "vocab_size": 158464
+}

configuration_zhinao.py

@@ -0,0 +1,92 @@
+# Copyright (c) 360zhinao and the HuggingFace Inc. team. All rights reserved.
+# This code is built upon Huggingface's transformers repository.
+
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__) 
+
+
+class ZhinaoConfig(PretrainedConfig):
+
+    model_type = "zhinao"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=None,
+        eos_token_id=None,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        bf16 = False,
+        fp16 = False,
+        use_flash_attn="auto",
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps 
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        self.bf16 = bf16
+        self.fp16 = fp16
+        self.use_flash_attn = use_flash_attn
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "ntk"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")

generation_config.json

@@ -0,0 +1,12 @@
+{
+    "do_sample": true,
+    "top_k": 0,
+    "top_p": 0.8,
+    "temperature": 1.0,
+    "repetition_penalty": 1.0,
+    "pad_token_id": 158326,
+    "eos_token_id": [158326, 158332, 158333],
+    "max_new_tokens": 1024,
+    "_from_model_config": true,
+    "transformers_version": "4.38.2"
+}

generation_utils.py

@@ -0,0 +1,186 @@
+import torch
+import numpy as np
+from queue import Queue
+from typing import Tuple, List, Union, Iterable
+from transformers.utils import logging, add_start_docstrings
+from transformers.generation.logits_process import LogitsProcessor, LOGITS_PROCESSOR_INPUTS_DOCSTRING, LogitsProcessorList
+
+
+def make_context(model, tokenizer, 
+                 messages: List[dict], 
+                 system: str = "You are a helpful assistant.",
+                 max_new_tokens: int=0, 
+                ):
+    
+    max_new_tokens = max_new_tokens or model.generation_config.max_new_tokens
+    max_input_length = model.config.model_max_length - max_new_tokens
+
+    im_start_id = [tokenizer.im_start_id]
+    im_end_id = [tokenizer.im_end_id]
+    nl_tokens = tokenizer.encode("\n")
+
+    def _tokenize_str(role, content):
+        return tokenizer.encode(role, allowed_special=set()) + nl_tokens + tokenizer.encode(content, allowed_special=set())
+    
+    def _parse_messages(messages):
+        system, query, history = "", "", []
+        ## system
+        if messages[0]["role"] == "system":
+            system = messages[0]["content"]
+            messages = messages[1:]
+        ## query
+        assert messages[-1]["role"] == "user"
+        query = messages[-1]["content"]
+        messages = messages[:-1]
+        ## history
+        assert len(messages) % 2 == 0
+        for i in range(0, len(messages), 2):
+            assert messages[i]["role"] == "user" and messages[i+1]["role"] == "assistant"
+            history.append([messages[i]["content"], messages[i+1]["content"]])
+
+        return system, query, history
+    
+    _system, query, history = _parse_messages(messages)
+
+    ## system
+    system_text = _system if _system != "" else system
+    system_tokens = []
+    if system_text:
+        system_tokens = im_start_id +  _tokenize_str("system", system_text) + im_end_id + nl_tokens
+    
+    ## query
+    query_tokens = im_start_id + _tokenize_str("user", query) + im_end_id + nl_tokens
+    ## final assistant
+    final_tokens = im_start_id + tokenizer.encode("assistant", allowed_special=set()) + nl_tokens
+    
+    ## max_history_tokens
+    max_history_length = max_input_length - len(system_tokens) - len(query_tokens) - len(final_tokens)
+    
+    ## history
+    context_tokens = []
+    for turn_query, turn_response in reversed(history):
+        ## query tokens
+        history_query_tokens = im_start_id + _tokenize_str("user", turn_query) + im_end_id + nl_tokens
+        ## answer tokens
+        histroy_response_tokens = im_start_id + _tokenize_str("assistant", turn_response)  + im_end_id + nl_tokens
+        ## this round tokens
+        next_context_tokens = history_query_tokens + histroy_response_tokens
+        ## concat
+        current_context_size = len(next_context_tokens) + len(context_tokens)
+        if current_context_size < max_history_length:
+            context_tokens = next_context_tokens + context_tokens
+        else:
+            break
+    input_tokens = system_tokens + context_tokens + query_tokens + final_tokens
+
+    return torch.LongTensor([input_tokens]).to(model.device)
+
+
+class TextIterStreamer:
+    def __init__(self, tokenizer, skip_prompt=False, skip_special_tokens=False):
+        self.tokenizer = tokenizer
+        self.skip_prompt = skip_prompt
+        self.skip_special_tokens = skip_special_tokens
+        self.tokens = []
+        self.text_queue = Queue()
+        self.next_tokens_are_prompt = True
+
+    def put(self, value):
+        if self.skip_prompt and self.next_tokens_are_prompt:
+            self.next_tokens_are_prompt = False
+        else:
+            if len(value.shape) > 1:
+                value = value[0]
+            self.tokens.extend(value.tolist())
+            self.text_queue.put(
+                self.tokenizer.decode(self.tokens, skip_special_tokens=self.skip_special_tokens, errors='ignore'))
+
+    def end(self):
+        self.text_queue.put(None)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        value = self.text_queue.get()
+        if value is None:
+            raise StopIteration()
+        else:
+            return value
+
+
+class OutputRepetitionPenaltyLogitsProcessor(LogitsProcessor):
+    r"""
+    [`OutputLogitsProcessor`] that prevents the repetition of previous tokens through a penalty. This penalty is applied at
+    most once per token. Note that, for decoder-only models like most LLMs, the considered tokens include the prompt.
+
+    In the original [paper](https://arxiv.org/pdf/1909.05858.pdf), the authors suggest the use of a penalty of around
+    1.2 to achieve a good balance between truthful generation and lack of repetition. To penalize and reduce
+    repetition, use `penalty` values above 1.0, where a higher value penalizes more strongly. To reward and encourage
+    repetition, use `penalty` values between 0.0 and 1.0, where a lower value rewards more strongly.
+
+    Args:
+        penalty (`float`):
+            The parameter for repetition penalty. 1.0 means no penalty. Above 1.0 penalizes previously generated
+            tokens. Between 0.0 and 1.0 rewards previously generated tokens.
+    """
+
+    def __init__(self, input_length: int, 
+                    presence_penalties: float = 1.0,
+                    frequency_penalties: float = 0,
+                    repetition_penalties: float = 0):
+        if not (repetition_penalties > 0):
+            raise ValueError(f"`repetition_penalties` has to be a strictly positive float, but is {repetition_penalties}")
+        if not ( (frequency_penalties >= -2) and (frequency_penalties <= 2) ):
+            raise ValueError(f"`frequency_penalties` has to be [-2, 2], but is {frequency_penalties}")
+        if not ( (presence_penalties >= -2) and (presence_penalties <= 2) ):
+            raise ValueError(f"`presence_penalties` has to be [-2, 2], but is {presence_penalties}")
+
+        self.repetition_penalties = repetition_penalties
+        self.frequency_penalties = frequency_penalties
+        self.presence_penalties = presence_penalties
+        self.input_length = input_length
+
+    def _get_bin_counts_and_mask(
+        self,
+        tokens: torch.Tensor,
+        vocab_size: int,
+        num_seqs: int,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Compute the bin counts for the tokens.
+        # vocab_size + 1 for padding.
+        bin_counts = torch.zeros((num_seqs, vocab_size + 1),
+                                dtype=torch.long,
+                                device=tokens.device)
+        bin_counts.scatter_add_(1, tokens, torch.ones_like(tokens))
+        bin_counts = bin_counts[:, :vocab_size]
+        mask = bin_counts > 0
+
+        return bin_counts, mask
+
+    @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
+    def __call__(self, input_ids: torch.LongTensor, logits: torch.FloatTensor) -> torch.FloatTensor:
+        prompt_tokens_tensor = input_ids[:, :self.input_length+1]
+        output_tokens_tensor = input_ids[:, self.input_length+1:]
+
+        num_seqs, vocab_size = logits.shape
+        _, prompt_mask = self._get_bin_counts_and_mask(
+            prompt_tokens_tensor, vocab_size, num_seqs)
+        output_bin_counts, output_mask = self._get_bin_counts_and_mask(
+            output_tokens_tensor, vocab_size, num_seqs)
+
+        repetition_penalties = torch.Tensor([self.repetition_penalties]).to(logits.device)
+        frequency_penalties = torch.Tensor([self.frequency_penalties]).to(logits.device)
+        presence_penalties = torch.Tensor([self.presence_penalties]).to(logits.device)
+
+        repetition_penalties = repetition_penalties[:, None].repeat(1, vocab_size)
+        repetition_penalties[~(prompt_mask | output_mask)] = 1.0
+        logits = torch.where(logits > 0, logits / repetition_penalties,
+                            logits * repetition_penalties)
+
+        # We follow the definition in OpenAI API.
+        # Refer to https://platform.openai.com/docs/api-reference/parameter-details
+        logits -= frequency_penalties.unsqueeze_(dim=1) * output_bin_counts
+        logits -= presence_penalties.unsqueeze_(dim=1) * output_mask
+
+        return logits

gptq_model-4bit-128g.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18f58ec70a3d8bb7b5a31e53a6259e96e2513a45d8d278a14b3ea4c725d19a8e
+size 5965669776

modeling_zhinao.py

@@ -0,0 +1,1056 @@
+# Copyright (c) 360zhinao and the HuggingFace Inc. team. All rights reserved.
+# This code is built upon Huggingface's transformers repository.
+
+import math
+import warnings
+from threading import Thread
+from typing import List, Optional, Tuple, Union
+    
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from transformers.generation.utils import GenerationConfig
+from transformers.generation.logits_process import LogitsProcessorList
+from .configuration_zhinao import ZhinaoConfig
+from .generation_utils import TextIterStreamer, make_context, OutputRepetitionPenaltyLogitsProcessor
+
+ 
+try:
+    from flash_attn import flash_attn_varlen_func 
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input 
+except:
+    flash_attn_varlen_func = None
+    index_first_axis, pad_input, unpad_input = None, None, None
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "ZhinaoConfig"
+ 
+
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class ZhinaoRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        ZhinaoRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class ZhinaoRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+class ZhinaoLinearScalingRotaryEmbedding(ZhinaoRotaryEmbedding):
+    """ZhinaoRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+class ZhinaoDynamicNTKScalingRotaryEmbedding(ZhinaoRotaryEmbedding):
+    """ZhinaoRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+class ZhinaoNTKScalingRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, scaling_factor=100, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base * scaling_factor 
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class ZhinaoMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x): 
+        intermediate = self.act_fn(self.gate_proj(x)) * self.up_proj(x)
+        down_proj = self.down_proj(intermediate) 
+        return down_proj
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class ZhinaoAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: ZhinaoConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+        self.dropout = 0.0
+        self.use_flash_attn = config.use_flash_attn
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.qkv_hidden_size = (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim
+        self.qkv_proj = nn.Linear(self.hidden_size, self.qkv_hidden_size, bias=True)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = ZhinaoRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = ZhinaoLinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = ZhinaoDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "ntk":
+                self.rotary_emb = ZhinaoNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+    
+    def raw_attention(self, query_states, key_states, value_states, attention_mask): 
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+ 
+        if attention_mask is not None: 
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states) 
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        
+        return attn_output
+    
+    def flash_attention(self, query_states, key_states, value_states, attention_mask):
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        batch_size, query_length = query_states.shape[0], query_states.shape[1]
+        query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+            query_states, key_states, value_states, attention_mask, query_length
+        )
+
+        cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+        max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+        attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=self.dropout,
+                    softmax_scale=None,
+                    causal=self.is_causal,
+                )
+        attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        return attn_output
+    
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        mixed_x_layer = self.qkv_proj(hidden_states) 
+        new_tensor_shape = mixed_x_layer.size()[:-1] + \
+            (self.num_key_value_heads, ((self.num_heads // self.num_key_value_heads + 2) * self.head_dim)) 
+        mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) 
+        query, key_states, value_states = torch.split(
+            mixed_x_layer, 
+            [self.num_heads // self.num_key_value_heads * self.head_dim, self.head_dim, self.head_dim],
+            dim=3
+        ) 
+        # [sq, b, ng, np/ng * hn] -> [sq, b, np, hn]
+        query_states = query.contiguous().view(query.size(0), query.size(1), -1, self.head_dim)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) 
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # q, k, v: [b, n, s, h] 
+        # check attention mask
+        if self.use_flash_attn:
+            if attention_mask is not None and attention_mask.size() != (bsz, kv_seq_len): 
+                raise ValueError(f"Attention mask should be of size {(bsz, kv_seq_len)}, but is {attention_mask.size()}")
+            attn_output = self.flash_attention(query_states, key_states, value_states, attention_mask) 
+        else:
+            if attention_mask is not None and attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(f"Attention mask should be of size {bsz, 1, q_len, kv_seq_len}, but is {attention_mask.size()}")
+            attn_output = self.raw_attention(query_states, key_states, value_states, attention_mask) 
+    
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape 
+
+        # On the first iteration we need to properly re-create the padding mask
+        # by slicing it on the proper place
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+ 
+
+class ZhinaoDecoderLayer(nn.Module):
+    def __init__(self, config: ZhinaoConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size   
+        
+        self.self_attn = ZhinaoAttention(config=config)
+        self.mlp = ZhinaoMLP(config)
+        self.input_layernorm = ZhinaoRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = ZhinaoRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+        
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+        
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+            
+        return outputs
+
+
+class ZhinaoPreTrainedModel(PreTrainedModel):
+    config_class = ZhinaoConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["ZhinaoDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, ZhinaoModel):
+            module.gradient_checkpointing = value
+
+
+class ZhinaoModel(ZhinaoPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`ZhinaoDecoderLayer`]
+
+    Args:
+        config: ZhinaoConfig
+    """
+
+    def __init__(self, config: ZhinaoConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.use_flash_attn = config.use_flash_attn
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([ZhinaoDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = ZhinaoRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+        
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+            )
+        
+        # (batch_size, 1, seq_length, seq_length)` if default attention is used
+        if not self.use_flash_attn:
+            attention_mask = self._prepare_decoder_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None 
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) 
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,) 
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class ZhinaoForCausalLM(ZhinaoPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = ZhinaoModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing 
+        if config.bf16:
+            self.model.bfloat16()
+            self.lm_head.bfloat16()
+        if config.fp16:
+            self.model.half()
+            self.lm_head.half()
+
+        if config.use_flash_attn == "auto":
+            if flash_attn_varlen_func:
+                if config.bf16 or config.fp16:
+                    logger.warn("Try importing flash-attention.")
+                    config.use_flash_attn = True
+                else:
+                    config.use_flash_attn = False
+                    logger.warn("Flash attention will be disabled because it does NOT support fp32.")
+            else:
+                config.use_flash_attn = False
+                logger.warn("Please install FlashAttention first, " "e.g., with pip install flash-attn")                   
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) 
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0] 
+        logits = self.lm_head(hidden_states)
+        
+        # warn：Huge gpu memory
+        logits = logits.float() 
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output 
+        
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        ) 
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+    
+
+    def generate(                                                                                                                                                       
+        self,
+        inputs: Optional[torch.Tensor] = None,
+        generation_config: Optional[GenerationConfig] = None,
+        streamer = None,
+        **kwargs,
+    ):
+        repetition_penalty = kwargs.pop("repetition_penalty", generation_config.repetition_penalty)
+        generation_config.repetition_penalty = 1.0
+        
+        logits_processor = None
+        if repetition_penalty > 1.0:
+            warnings.warn("We highly recommend using OpenAI's frequency and presence penalty instead of the original repetition penalty. The original repetition penalty penalizes prompt tokens, which may lead to various potential issues. Therefore, your repetition penalty coefficient will be transformed into frequency penalty and presence penalty.", UserWarning)
+            presence_penalty  = repetition_penalty - 1.0
+            frequency_penalty = repetition_penalty - 1.0
+            logits_processor = LogitsProcessorList(
+                [OutputRepetitionPenaltyLogitsProcessor(inputs.size(1), presence_penalty, frequency_penalty, 1.0)]
+            )
+        
+        response = super().generate(
+            inputs,
+            generation_config=generation_config,
+            logits_processor=logits_processor,
+            streamer=streamer,
+            **kwargs,
+        )
+        generation_config.repetition_penalty = repetition_penalty
+        return response
+
+    
+    def chat(
+            self, 
+            tokenizer, 
+            messages: List[dict],
+            system: str = "You are a helpful assistant.",
+            stream=False,
+            generation_config: Optional[GenerationConfig]=None):
+        
+        generation_config = generation_config or self.generation_config
+        input_ids = make_context(
+            model=self, tokenizer=tokenizer, messages=messages,
+            system=system, max_new_tokens=generation_config.max_new_tokens
+        )
+
+        if stream:
+            streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+            Thread(target=self.generate, kwargs=dict(
+                inputs=input_ids, streamer=streamer,
+                generation_config=generation_config,
+            )).start()
+            return streamer
+        else:
+            outputs = self.generate(input_ids, generation_config=generation_config)
+            response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
+        
+        return response
+
+
+class ZhinaoForSequenceClassification(ZhinaoPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = ZhinaoModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to(
+                    logits.device
+                )
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

quantize_config.json

@@ -0,0 +1,13 @@
+{
+  "bits": 4,
+  "group_size": 128,
+  "damp_percent": 0.01,
+  "desc_act": false,
+  "static_groups": false,
+  "sym": true,
+  "true_sequential": true,
+  "model_name_or_path": "360Zhinao-7B-Chat-4K-Int4",
+  "model_file_base_name": "gptq_model-4bit-128g",
+  "quant_method": "gptq",
+  "checkpoint_format": "gptq"
+}

tokenization_zhinao.py

@@ -0,0 +1,279 @@
+import os
+import torch
+import base64
+import tiktoken
+from typing import Collection, Optional, Dict, List, Set, Tuple, Union
+from transformers import PreTrainedTokenizer
+from transformers.utils import PaddingStrategy
+from transformers.tokenization_utils import PreTrainedTokenizer
+
+
+PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
+
+
+class SPTokenizer:
+    def __init__(self, model_path):
+        self.vocab_file = model_path
+        self.pad_token = '<pad>'
+        self.unk_token = '<unk>'
+        self.mask_token = '<mask>'
+        self.eod_token = '<eod>'
+        self.eop_token = '<eop>'
+        self.im_start_token = '<|im_start|>'
+        self.im_end_token = '<|im_end|>'
+
+        ## special_tokens
+        self.SPECIAL_TOKENS = (
+            self.pad_token,
+            self.unk_token,
+            self.mask_token,
+            self.eod_token,
+            self.eop_token,
+            '[space2]', '[space3]', '[space4]', '[space8]',
+            self.im_start_token, self.im_end_token
+        )
+        self.bulid_tokenizer()
+        self.out = self.output_core_token()
+        
+        self.token2strs = {
+            "[space2]": "  ",
+            "[space3]": "   ",
+            "[space4]": "    ",
+            "[space8]": "        ",
+        }
+        self.str2tokens = {v: k for k, v in self.token2strs.items()}
+        self.sorted_strs = sorted(list(self.str2tokens.keys()),
+                                  key=lambda x: len(x), reverse=True)
+        
+        ## skip_special_tokens
+        self.decode_skip_special_tokens = [
+            self.pad_token,
+            self.unk_token,
+            self.mask_token,
+            self.eod_token,
+            self.eop_token,
+            self.im_start_token,
+            self.im_end_token]
+        self.decode_skip_special_tokens_ids = [self.convert_token_to_id(token) for token in self.decode_skip_special_tokens]
+
+    def _load_tiktoken_bpe(self, tiktoken_bpe_file: str):
+        with open(tiktoken_bpe_file, "rb") as f:
+            contents = f.read()
+        return {
+            base64.b64decode(token): int(rank)
+            for token, rank in (line.split() for line in contents.splitlines() if line)
+        }
+    
+    def bulid_tokenizer(self):
+        mergeable_ranks = self._load_tiktoken_bpe(self.vocab_file)
+        special_tokens = {
+            token: index
+            for index, token in enumerate(
+                self.SPECIAL_TOKENS, start=len(mergeable_ranks)
+            )
+        }
+        encode = tiktoken.Encoding(
+            "zhinao",
+            pat_str=PAT_STR,
+            mergeable_ranks=mergeable_ranks,
+            special_tokens=special_tokens
+        )
+        decoder = {v: k for k, v in mergeable_ranks.items()}
+        decoder.update({v: k for k, v in special_tokens.items()})
+        decoder_token2id = {v: k for k, v in decoder.items()}
+    
+        self.tokenizer = encode
+        self.decoder = decoder
+        self.decoder_token2id = decoder_token2id
+        self.num_tokens = len(mergeable_ranks) + len(self.SPECIAL_TOKENS)
+
+    def output_core_token(self):
+        """output special tokens"""
+        out = {}
+        for t in self.SPECIAL_TOKENS:
+            out[t] = self.convert_token_to_id(t)
+        return out
+
+    def tokenize(
+            self, 
+            text, 
+            allowed_special: Union[Set, str] = "all",
+            disallowed_special: Union[Collection, str] = ()):
+        tokens = []
+        text = self.convert(text)
+        for idx in self.tokenizer.encode(text, allowed_special=allowed_special, disallowed_special=disallowed_special):
+            tokens.append(self.decoder[idx])
+        return tokens
+
+    def encode(self, text, allowed_special="all", disallowed_special=()):
+        """text to id"""
+        text = self.convert(text)
+        return self.tokenizer.encode(text, allowed_special=allowed_special, disallowed_special=disallowed_special)
+    
+    def decode(self, ids, errors="replace"):
+        """id to text"""
+        text = self.tokenizer.decode(ids, errors=errors)
+        return self.deconvert(text)
+
+    def decode_tokens(self, tokens: List[str]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        text = ""
+        temp = b""
+        for t in tokens:
+            if isinstance(t, str):
+                if temp:
+                    text += temp.decode("utf-8", errors="replace")
+                    temp = b""
+                text += t
+            elif isinstance(t, bytes):
+                temp += t
+            else:
+                raise TypeError("token should only be of type bytes or str")
+        if temp:
+            text += temp.decode("utf-8", errors="replace")
+        return self.deconvert(text)
+
+    def convert_id_to_token(self, idx):
+        return self.decoder[idx]
+    
+    def convert_token_to_id(self, token):
+        return self.decoder_token2id[token]
+
+    def convert(self, text):
+        """将文本的特殊字符转换成特殊token"""
+        for k in ["[br]", "<br>"]:
+            text = text.replace(k, "\n")
+        for k in self.sorted_strs:
+            if k in text:
+                text = text.replace(k, self.str2tokens[k])
+        return text
+
+    def deconvert(self, text):
+        """将解码文本恢复原始字符"""
+        for t in self.token2strs:
+            if t in text:
+                text = text.replace(t, self.token2strs[t])
+        return text
+
+
+class ZhinaoTokenizer(PreTrainedTokenizer):
+    vocab_files_names = {"vocab_file": "vocab/360.tiktoken"}
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, **kwargs):
+        self.name = "ZhinaoTokenizer"
+        self.errors = "replace"
+        self.vocab_file = vocab_file
+        self.tokenizer = SPTokenizer(model_path=vocab_file)
+        try:
+            kwargs.pop('eos_token')
+            kwargs.pop('pad_token')
+            kwargs.pop('unk_token')
+        except:
+            pass
+        super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs)
+        self.pad_token_id = self.tokenizer.convert_token_to_id(self.tokenizer.pad_token)
+        self.eod_id = self.tokenizer.convert_token_to_id(self.tokenizer.eod_token)
+        self.im_start_id = self.tokenizer.convert_token_to_id(self.tokenizer.im_start_token)
+        self.im_end_id = self.tokenizer.convert_token_to_id(self.tokenizer.im_end_token)
+        from icecream import ic
+        ic(
+            self.eos_token_id,
+            self.pad_token_id,
+            self.im_start_id,
+            self.im_end_id)
+
+    @property
+    def unk_token(self) -> str:
+        return self.tokenizer.unk_token
+
+    @property
+    def pad_token(self) -> str:
+        return self.tokenizer.pad_token
+
+    @property
+    def eos_token(self) -> str:
+        return self.tokenizer.eod_token
+
+    @property
+    def eos_token_id(self):
+        return self.tokenizer.convert_token_to_id(self.tokenizer.eod_token)
+
+    @property
+    def eop_token(self) -> str:
+        return self.tokenizer.eop_token
+
+    @property
+    def eop_token_id(self):
+        return self.tokenizer.convert_token_to_id(self.tokenizer.eop_token)
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.num_tokens
+
+    def get_vocab(self):
+        """ Returns vocab as a dict """
+        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+    
+    def tokenize(
+        self,
+        text: str,
+        allowed_special: Union[Set, str] = "all",
+        disallowed_special: Union[Collection, str] = (),
+    ) -> List[Union[bytes, str]]:
+        tokens = []
+        for t in self.tokenizer.encode(
+            text, allowed_special=allowed_special, disallowed_special=disallowed_special
+        ):
+            tokens.append(self.tokenizer.decoder[t])
+        return tokens
+    
+    def _decode(
+        self,
+        token_ids: Union[int, List[int]],
+        skip_special_tokens: bool = False,
+        errors: str = None,
+        **kwargs,
+    ) -> str:
+        if isinstance(token_ids, int):
+            token_ids = [token_ids]
+        if skip_special_tokens:
+            token_ids = [i for i in token_ids if i not in self.tokenizer.decode_skip_special_tokens_ids]
+        return self.tokenizer.decode(token_ids, errors=errors or self.errors)
+
+    def _tokenize(self, text, **kwargs):
+        raise NotImplementedError
+
+    def _convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        return self.tokenizer.convert_token_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab. """
+        return self.tokenizer.convert_id_to_token(index)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        return self.tokenizer.decode_tokens(tokens)
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        """Save only the vocabulary of the tokenizer (vocabulary). """
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(save_directory, self.vocab_files_names["vocab_file"])
+        else:
+            vocab_file = save_directory
+
+        with open(self.vocab_file, 'rb') as fin:
+            proto_str = fin.read()
+
+        os.makedirs(save_directory + "/vocab", exist_ok=True)
+        with open(vocab_file, "wb") as writer:
+            writer.write(proto_str)
+
+        return (vocab_file,)

tokenizer_config.json

@@ -0,0 +1,18 @@
+{
+  "added_tokens_decoder": {},
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_zhinao.ZhinaoTokenizer",
+      null
+    ]
+  },
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": false,
+  "eos_token": "<eod>",
+  "model_max_length": 4096,
+  "pad_token": "<pad>",
+  "padding_side": "left",
+  "remove_space": false,
+  "tokenizer_class": "ZhinaoTokenizer",
+  "unk_token": "<unk>"
+}