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Baichuan2-13B-Chat/README.md

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+---
+language:
+  - en
+  - zh
+license: other
+tasks:
+  - text-generation
+studios:
+  - baichuan-inc/Baichuan-13B-Chatdemo
+---
+
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<div align="center">
+<h1>
+  Baichuan 2
+</h1>
+</div>
+
+<div align="center">
+  <a href="https://github.com/baichuan-inc/Baichuan2" target="_blank">🦉GitHub</a> | 
+  <a href="https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Base/file/view/master/wechat.jpeg" target="_blank">💬WeChat</a> | 
+  <a href="https://modelscope.cn/studios/baichuan-inc/Baichuan-13B-Chatdemo/summary" target="_blank">🤖Demo</a> 
+</div>
+<div align="center">
+🚀 <a href="https://www.baichuan-ai.com/" target="_blank">百川大模型在线对话平台</a> 已正式向公众开放 🎉
+</div>
+
+# 目录
+
+- [📖 模型介绍](#模型介绍)
+- [⚙️ 快速开始](#快速开始)
+- [📊 Benchmark评估](#评估)
+- [📜 声明与协议](#声明与协议)
+
+# 模型介绍
+
+- Baichuan 2 是[百川智能]推出的**新一代开源大语言模型**，采用 **2.6 万亿**  Tokens 的高质量语料训练。
+- Baichuan 2 在多个权威的中文、英文和多语言的通用、领域 benchmark 上取得同尺寸**最佳**的效果。
+- 本次发布包含有 **7B**、**13B** 的 **Base** 和 **Chat** 版本，并提供了 Chat 版本的 **4bits 量化**。
+- 所有版本对学术研究完全开放。同时，开发者通过邮件申请并获得官方商用许可后，即可**免费商用**，请参考[协议](#协议)章节。
+- 欢迎阅读我们的技术报告 [Baichuan 2: Open Large-scale Language Models] 获取更多信息。
+
+本次发布版本和下载链接见下表：
+
+|     |          基座模型        |          对齐模型        |       对齐模型 4bits 量化        |
+|:---:|:--------------------:|:--------------------:|:--------------------------:|
+| 7B  | [Baichuan2-7B-Base]  | [Baichuan2-7B-Chat]  | [Baichuan2-7B-Chat-4bits]  |
+| 13B | [Baichuan2-13B-Base] | [Baichuan2-13B-Chat] | [Baichuan2-13B-Chat-4bits] |
+
+# 快速开始
+
+```python
+import torch
+from modelscope import snapshot_download, AutoModelForCausalLM, AutoTokenizer,GenerationConfig
+model_dir = snapshot_download("baichuan-inc/Baichuan2-13B-Chat", revision='v1.0.1')
+tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", 
+                              trust_remote_code=True, torch_dtype=torch.float16)
+model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", 
+                              trust_remote_code=True, torch_dtype=torch.float16)
+model.generation_config = GenerationConfig.from_pretrained(model_dir)
+messages = []
+messages.append({"role": "user", "content": "讲解一下“温故而知新”"})
+response = model.chat(tokenizer, messages)
+print(response)
+messages.append({'role': 'assistant', 'content': response})
+messages.append({"role": "user", "content": "背诵一下将进酒"})
+response = model.chat(tokenizer, messages)
+print(response)
+```
+在魔搭社区的免费算力上，也可以通过量化的方式使用13B对话模型
+```python
+import torch
+from modelscope import snapshot_download, AutoModelForCausalLM, AutoTokenizer,GenerationConfig
+from transformers import BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(
+    False,
+    True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_use_double_quant=True)
+model_dir = snapshot_download("baichuan-inc/Baichuan2-13B-Chat", revision='v1.0.1')
+tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", 
+                              trust_remote_code=True, torch_dtype=torch.float16)
+model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", 
+                              trust_remote_code=True, torch_dtype=torch.float16,
+                              quantization_config=quantization_config)
+model.generation_config = GenerationConfig.from_pretrained(model_dir)
+messages = []
+messages.append({"role": "user", "content": "讲解一下“温故而知新”"})
+response = model.chat(tokenizer, messages)
+print(response)
+messages.append({'role': 'assistant', 'content': response})
+messages.append({"role": "user", "content": "背诵一下将进酒"})
+response = model.chat(tokenizer, messages)
+print(response)
+```
+# Benchmark 结果
+
+我们在[通用]、[法律]、[医疗]、[数学]、[代码]和[多语言翻译]六个领域的中英文权威数据集上对模型进行了广泛测试，更多详细测评结果可查看[GitHub]。
+
+### 7B 模型结果
+
+|                         | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** |
+|:-----------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:|
+|                         |  5-shot    |  5-shot  |  5-shot   | 5-shot     | 5-shot      | 3-shot  |
+|        **GPT-4**        | 68.40      | 83.93    | 70.33     | 66.15      | 63.27       | 75.12   |
+|    **GPT-3.5 Turbo**    | 51.10      | 68.54    | 54.06     | 47.07      | 46.13       | 61.59   |
+|      **LLaMA-7B**       | 27.10      | 35.10    | 26.75     | 27.81      | 28.17       | 32.38   |
+|      **LLaMA2-7B**      | 28.90      | 45.73    | 31.38     | 25.97      | 26.53       | 39.16   |
+|       **MPT-7B**        | 27.15      | 27.93    | 26.00     | 26.54      | 24.83       | 35.20   |
+|      **Falcon-7B**      | 24.23      | 26.03    | 25.66     | 24.24      | 24.10       | 28.77   |
+|     **ChatGLM2-6B**     | 50.20      | 45.90    | 49.00     | 49.44      | 45.28       | 31.65   |
+|    **[Baichuan-7B]**    | 42.80      | 42.30    | 44.02     | 36.34      | 34.44       | 32.48   |
+| **[Baichuan2-7B-Base]** | 54.00      | 54.16    | 57.07     | 47.47      | 42.73       | 41.56   |
+
+### 13B 模型结果
+
+|                             | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** |
+|:---------------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:|
+|                             |  5-shot    |  5-shot  |  5-shot   | 5-shot     | 5-shot      | 3-shot  |
+|          **GPT-4**          | 68.40      | 83.93    | 70.33     | 66.15      | 63.27       | 75.12   |
+|      **GPT-3.5 Turbo**      | 51.10      | 68.54    | 54.06     | 47.07      | 46.13       | 61.59   |
+|        **LLaMA-13B**        | 28.50      | 46.30    | 31.15     | 28.23      | 28.22       | 37.89   |
+|       **LLaMA2-13B**        | 35.80      | 55.09    | 37.99     | 30.83      | 32.29       | 46.98   |
+|       **Vicuna-13B**        | 32.80      | 52.00    | 36.28     | 30.11      | 31.55       | 43.04   |
+| **Chinese-Alpaca-Plus-13B** | 38.80      | 43.90    | 33.43     | 34.78      | 35.46       | 28.94   |
+|       **XVERSE-13B**        | 53.70      | 55.21    | 58.44     | 44.69      | 42.54       | 38.06   |
+|  **[Baichuan-13B-Base]**    | 52.40      | 51.60    | 55.30     | 49.69      | 43.20       | 43.01   |
+|  **[Baichuan2-13B-Base]**   | 58.10      | 59.17    | 61.97     | 54.33      | 48.17       | 48.78   |
+
+
+## 训练过程模型
+
+除了训练了 2.6 万亿 Tokens 的 [Baichuan2-7B-Base] 模型，我们还提供了在此之前的另外 11 个中间过程的模型（分别对应训练了约 0.2 ~ 2.4 万亿 Tokens）供社区研究使用（[训练过程heckpoint下载]）。下图给出了这些 checkpoints 在 C-Eval、MMLU、CMMLU 三个 benchmark 上的效果变化：
+
+![checkpoint](https://modelscope.cn/api/v1/models/baichuan-inc/Baichuan2-7B-Base/repo?Revision=master&FilePath=media/checkpoints.jpeg&View=true)
+
+# 声明与协议
+
+## 声明
+
+我们在此声明，我们的开发团队并未基于 Baichuan 2 模型开发任何应用，无论是在 iOS、Android、网页或任何其他平台。我们强烈呼吁所有使用者，不要利用
+Baichuan 2 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Baichuan 2
+模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。
+
+我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用
+Baichuan 2 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。
+
+## 协议
+
+* Baichuan 2 模型的社区使用需遵循[《Baichuan 2 模型社区许可协议》]。
+* Baichuan 2 支持商用，如果将 Baichuan 2 模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱 [[email protected]]。
+
+[GitHub]:https://github.com/baichuan-inc/Baichuan2
+[Baichuan2]:https://github.com/baichuan-inc/Baichuan2
+
+[Baichuan-7B]:https://modelscope.cn/models/baichuan-inc/baichuan-7B/summary
+[Baichuan2-7B-Base]:https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Base/summary
+[Baichuan2-7B-Chat]:https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat/summary
+[Baichuan2-7B-Chat-4bits]:https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat-4bits/summary
+[Baichuan-13B-Base]:https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Base/summary
+[Baichuan2-13B-Base]:https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Base/summary
+[Baichuan2-13B-Chat]:https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat/summary
+[Baichuan2-13B-Chat-4bits]:https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat-4bits/summary
+
+[通用]:https://github.com/baichuan-inc/Baichuan2#%E9%80%9A%E7%94%A8%E9%A2%86%E5%9F%9F
+[法律]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97
+[医疗]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97
+[数学]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81
+[代码]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81
+[多语言翻译]:https://github.com/baichuan-inc/Baichuan2#%E5%A4%9A%E8%AF%AD%E8%A8%80%E7%BF%BB%E8%AF%91
+
+[《Baichuan 2 模型社区许可协议》]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Baichuan2%20%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf
+
+[邮件申请]: mailto:[email protected]
+[Email]: mailto:[email protected]
+[[email protected]]: mailto:[email protected]
+[训练过程heckpoint下载]: https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints
+[百川智能]: https://www.baichuan-ai.com
+[Baichuan 2: Open Large-scale Language Models]:https://cdn.baichuan-ai.com/paper/Baichuan2-technical-report.pdf

Baichuan2-13B-Chat/config.json

@@ -0,0 +1,28 @@
+{
+  "_from_model_config": true,
+  "architectures": [
+    "BaichuanForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_baichuan.BaichuanConfig",
+    "AutoModelForCausalLM": "modeling_baichuan.BaichuanForCausalLM"
+  },
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13696,
+  "model_max_length": 4096,
+  "model_type": "baichuan",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.29.2",
+  "use_cache": true,
+  "vocab_size": 125696
+}

Baichuan2-13B-Chat/configuration.json

@@ -0,0 +1,11 @@
+{
+    "framework": "pytorch",
+    "task": "text-generation",
+    "model": {
+        "type": "Baichuan2-13B-Chat"
+    },
+    "pipeline": {
+        "type": "Baichuan2-13B-chatbot-pipe"
+    },
+    "allow_remote": true
+}

Baichuan2-13B-Chat/configuration_baichuan.py

@@ -0,0 +1,48 @@
+# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class BaichuanConfig(PretrainedConfig):
+    model_type = "baichuan"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=64000,
+        hidden_size=5120,
+        intermediate_size=13696,
+        num_hidden_layers=40,
+        num_attention_heads=40,
+        hidden_act="silu",
+        model_max_length=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        gradient_checkpointing=False,
+        z_loss_weight=0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.model_max_length = model_max_length
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.z_loss_weight = z_loss_weight
+        self.gradient_checkpointing = (gradient_checkpointing,)
+        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,
+        )

Baichuan2-13B-Chat/generation_config.json

@@ -0,0 +1,14 @@
+{
+  "pad_token_id": 0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "user_token_id": 195,
+  "assistant_token_id": 196,
+  "max_new_tokens": 2048,
+  "temperature": 0.3,
+  "top_k": 5,
+  "top_p": 0.85,
+  "repetition_penalty": 1.05,
+  "do_sample": true,
+  "transformers_version": "4.29.2"
+}

Baichuan2-13B-Chat/generation_utils.py

@@ -0,0 +1,83 @@
+from typing import List
+from queue import Queue
+
+import torch
+
+
+def build_chat_input(model, tokenizer, messages: List[dict], max_new_tokens: int=0):
+    def _parse_messages(messages, split_role="user"):
+        system, rounds = "", []
+        round = []
+        for i, message in enumerate(messages):
+            if message["role"] == "system":
+                assert i == 0
+                system = message["content"]
+                continue
+            if message["role"] == split_role and round:
+                rounds.append(round)
+                round = []
+            round.append(message)
+        if round:
+            rounds.append(round)
+        return system, rounds
+
+    max_new_tokens = max_new_tokens or model.generation_config.max_new_tokens
+    max_input_tokens = model.config.model_max_length - max_new_tokens
+    system, rounds = _parse_messages(messages, split_role="user")
+    system_tokens = tokenizer.encode(system)
+    max_history_tokens = max_input_tokens - len(system_tokens)
+
+    history_tokens = []
+    for round in rounds[::-1]:
+        round_tokens = []
+        for message in round:
+            if message["role"] == "user":
+                round_tokens.append(model.generation_config.user_token_id)
+            else:
+                round_tokens.append(model.generation_config.assistant_token_id)
+            round_tokens.extend(tokenizer.encode(message["content"]))
+        if len(history_tokens) == 0 or len(history_tokens) + len(round_tokens) <= max_history_tokens:
+            history_tokens = round_tokens + history_tokens  # concat left
+            if len(history_tokens) < max_history_tokens:
+                continue
+        break
+
+    input_tokens = system_tokens + history_tokens
+    if messages[-1]["role"] != "assistant":
+        input_tokens.append(model.generation_config.assistant_token_id)
+    input_tokens = input_tokens[-max_input_tokens:]  # truncate left
+    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))
+
+    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
+

Baichuan2-13B-Chat/modeling_baichuan.py

@@ -0,0 +1,827 @@
+# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
+
+from .configuration_baichuan import BaichuanConfig
+from .generation_utils import build_chat_input, TextIterStreamer
+
+import math
+from threading import Thread
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from torch.nn import functional as F
+from transformers import PreTrainedModel, PretrainedConfig
+from transformers.activations import ACT2FN
+from transformers.generation.utils import GenerationConfig
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.utils import logging, ContextManagers
+
+import os
+from contextlib import contextmanager
+from accelerate import init_empty_weights
+
+logger = logging.get_logger(__name__)
+
+try:
+    from xformers import ops as xops
+except ImportError:
+    xops = None
+    logger.warning(
+        "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers."
+    )
+
+
+def _get_interleave(n):
+    def _get_interleave_power_of_2(n):
+        start = 2 ** (-(2 ** -(math.log2(n) - 3)))
+        ratio = start
+        return [start * ratio**i for i in range(n)]
+
+    if math.log2(n).is_integer():
+        return _get_interleave_power_of_2(n)
+    else:
+        closest_power_of_2 = 2 ** math.floor(math.log2(n))
+        return (
+            _get_interleave_power_of_2(closest_power_of_2)
+            + _get_interleave(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
+        )
+
+
+def _fill_with_neg_inf(t):
+    """FP16-compatible function that fills a tensor with -inf."""
+    return t.float().fill_(float("-inf")).type_as(t)
+
+
+def _buffered_future_mask(tensor, maxpos, alibi, attn_heads):
+    _future_mask = torch.triu(_fill_with_neg_inf(torch.zeros([maxpos, maxpos])), 1)
+    _future_mask = _future_mask.unsqueeze(0) + alibi
+    new_future_mask = _future_mask.to(tensor)
+    return new_future_mask[: tensor.shape[0] * attn_heads, :maxpos, :maxpos]
+
+
+def _gen_alibi_mask(tensor, n_head, max_pos):
+    slopes = torch.Tensor(_get_interleave(n_head))
+    position_point = torch.arange(max_pos) - max_pos + 1
+    position_point = position_point.unsqueeze(0).unsqueeze(0).expand(n_head, -1, -1)
+    diag = torch.diag(position_point[0])
+    position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(-1, -2)
+    alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
+    alibi = alibi.view(n_head, 1, max_pos)
+    alibi_mask = torch.triu(_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1)
+    alibi_mask = alibi_mask.unsqueeze(0) + alibi
+    return alibi_mask
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, hidden_size, epsilon=1e-6):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(hidden_size))
+        self.epsilon = epsilon
+
+    def forward(self, hidden_states):
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.epsilon)
+
+        # convert into half-precision
+        if self.weight.dtype in [torch.float16, torch.bfloat16]:
+            hidden_states = hidden_states.to(self.weight.dtype)
+
+        return self.weight * hidden_states
+
+
+class MLP(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+    ):
+        super().__init__()
+        self.gate_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = torch.nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.up_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class BaichuanAttention(torch.nn.Module):
+    def __init__(self, config: BaichuanConfig):
+        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.max_position_embeddings = config.model_max_length
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size {self.hidden_size} is not divisible by num_heads {self.num_heads}"
+            )
+        self.W_pack = torch.nn.Linear(
+            self.hidden_size, 3 * self.hidden_size, bias=False
+        )
+        self.o_proj = torch.nn.Linear(
+            self.num_heads * self.head_dim, self.hidden_size, bias=False
+        )
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return (
+            tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
+            .transpose(1, 2)
+            .contiguous()
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = 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()
+
+        proj = self.W_pack(hidden_states)
+        proj = (
+            proj.unflatten(-1, (3, self.hidden_size))
+            .unsqueeze(0)
+            .transpose(0, -2)
+            .squeeze(-2)
+        )
+        query_states = (
+            proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        )
+        key_states = (
+            proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        )
+        value_states = (
+            proj[2].view(bsz, q_len, self.num_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]
+
+        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
+        if xops is not None and self.training:
+            attn_weights = None
+            # query_states = query_states.transpose(1, 2)
+            # key_states = key_states.transpose(1, 2)
+            # value_states = value_states.transpose(1, 2)
+            # attn_output = xops.memory_efficient_attention(
+            #     query_states, key_states, value_states, attn_bias=attention_mask
+            # )
+            with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
+                attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask = attention_mask)
+            attn_output = attn_output.transpose(1, 2)
+        else:
+            attn_weights = torch.matmul(
+                query_states, key_states.transpose(2, 3)
+            ) / math.sqrt(self.head_dim)
+
+            if attention_mask is not None:
+                if q_len == 1:  # inference with cache
+                    if len(attention_mask.size()) == 4:
+                        attention_mask = attention_mask[:, :, -1:, :]
+                    else:
+                        attention_mask = attention_mask[:, -1:, :]
+                attn_weights = attn_weights + attention_mask
+                attn_weights = torch.max(
+                    attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
+                )
+
+            attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
+            attn_output = torch.matmul(attn_weights, value_states)
+
+            attn_output = attn_output.transpose(1, 2)
+        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
+
+
+class BaichuanLayer(torch.nn.Module):
+    def __init__(self, config: BaichuanConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = BaichuanAttention(config=config)
+        self.mlp = MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, epsilon=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = 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]]
+    ]:
+        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,
+            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 use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class BaichuanPreTrainedModel(PreTrainedModel):
+    config_class = BaichuanConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["BaichuanLayer"]
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, torch.nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, torch.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, BaichuanModel):
+            module.gradient_checkpointing = value
+
+
+class BaichuanModel(BaichuanPreTrainedModel):
+    def __init__(self, config: BaichuanConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.n_head = config.num_attention_heads
+        self.embed_tokens = torch.nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
+        self.layers = torch.nn.ModuleList(
+            [BaichuanLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.norm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
+
+        self.gradient_checkpointing = config.gradient_checkpointing
+        self.post_init()
+        self.max_cache_pos = config.model_max_length
+        self.first_run = True
+        self.alibi_mask = None
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def get_alibi_mask(self, tensor, seq_length_with_past):
+        if self.training:
+            slopes = torch.Tensor(_get_interleave(self.n_head))
+            position_point = (
+                torch.arange(seq_length_with_past) - seq_length_with_past + 1
+            )
+            position_point = (
+                position_point.unsqueeze(0)
+                .unsqueeze(0)
+                .expand(self.n_head, seq_length_with_past, -1)
+            )
+            diag = torch.diag(position_point[0])
+            position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(
+                -1, -2
+            )
+            alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
+            mask = _buffered_future_mask(
+                tensor, seq_length_with_past, alibi, self.n_head
+            )
+        else:
+            if self.first_run:
+                self.first_run = False
+                self.register_buffer(
+                    "future_mask",
+                    _gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
+                        tensor
+                    ),
+                    persistent=False,
+                )
+            if seq_length_with_past > self.max_cache_pos:
+                self.max_cache_pos = seq_length_with_past
+                self.register_buffer(
+                    "future_mask",
+                    _gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
+                        tensor
+                    ),
+                    persistent=False,
+                )
+            mask = self.future_mask[
+                : self.n_head, :seq_length_with_past, :seq_length_with_past
+            ]
+        return mask
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = False,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot provide both input_ids and inputs_embeds simultaneously"
+            )
+        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 need to provide input_ids or inputs_embeds")
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        seq_length_with_past = seq_length
+
+        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 inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if self.training:
+            if (
+                self.alibi_mask is None
+                or self.alibi_mask.shape[-1] != seq_length_with_past
+            ):
+                self.alibi_mask = self.get_alibi_mask(
+                    inputs_embeds, seq_length_with_past
+                )
+            alibi_mask = self.alibi_mask
+        else:
+            alibi_mask = self.get_alibi_mask(inputs_embeds, seq_length_with_past)
+
+        if attention_mask is not None:
+            if len(attention_mask.shape) == 2:
+                expanded_mask = attention_mask.to(alibi_mask.dtype)
+                expanded_mask = torch.tril(
+                    torch.gt(expanded_mask[:, :, None] * expanded_mask[:, None, :], 0)
+                ) * torch.eq(expanded_mask[:, :, None] - expanded_mask[:, None, :], 0)
+            else:
+                expanded_mask = attention_mask
+            bsz = inputs_embeds.size(0)
+            src_len, tgt_len = alibi_mask.size()[-2:]
+            expanded_mask = (
+                expanded_mask.unsqueeze(1)
+                .expand(bsz, 1, src_len, tgt_len)
+                .to(alibi_mask.dtype)
+            )
+            inverted_mask = 1.0 - expanded_mask
+            inverted_mask = inverted_mask.masked_fill(
+                inverted_mask.to(torch.bool), torch.finfo(alibi_mask.dtype).min
+            )
+            attention_mask = inverted_mask + alibi_mask.unsqueeze(0)
+        else:
+            attention_mask = alibi_mask
+
+        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, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    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 NormHead(nn.Module):
+    def __init__(self, hidden_size, vocab_size, bias=False):
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
+        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        self.first_flag = True
+
+    def forward(self, hidden_states):
+        if self.training:
+            norm_weight = nn.functional.normalize(self.weight)
+            self.first_flag = True
+        elif self.first_flag:
+            self.first_flag = False
+            self.weight.data = nn.functional.normalize(self.weight)
+            norm_weight = self.weight
+        else:
+            norm_weight = self.weight
+        return nn.functional.linear(hidden_states, norm_weight)
+
+_init_weights = True
+@contextmanager
+def no_init_weights(_enable=True):
+    global _init_weights
+    old_init_weights = _init_weights
+    if _enable:
+        _init_weights = False
+    try:
+        yield
+    finally:
+        _init_weights = old_init_weights
+
+
+class BaichuanForCausalLM(BaichuanPreTrainedModel):
+    def __init__(self, config, *model_args, **model_kwargs):
+        super().__init__(config, *model_args, **model_kwargs)
+        self.model = BaichuanModel(config)
+        self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False)
+        #if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
+        if hasattr(config, "quantization_config") and isinstance(config.quantization_config, dict) and config.quantization_config.get('load_in_4bit', False):
+            try:
+                from .quantizer import quantize_offline, init_model_weight_int4
+            except ImportError:
+                raise ImportError(f"Needs quantize_offline to run quantize.")
+            quantize_offline(self, 4)
+        # 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 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
+    
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+        *model_args,
+        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
+        cache_dir: Optional[Union[str, os.PathLike]] = None,
+        ignore_mismatched_sizes: bool = False,
+        force_download: bool = False,
+        local_files_only: bool = False,
+        token: Optional[Union[str, bool]] = None,
+        revision: str = "main",
+        use_safetensors: bool = None,
+        **kwargs,
+    ):
+    
+        # Load config if we don't provide a configuration
+        if not isinstance(config, PretrainedConfig):
+            config_path = config if config is not None else pretrained_model_name_or_path
+            config, model_kwargs = cls.config_class.from_pretrained(
+                config_path,
+                cache_dir=cache_dir,
+                return_unused_kwargs=True,
+                force_download=force_download,
+                resume_download=False,
+                proxies=None,
+                local_files_only=local_files_only,
+                token=token,
+                revision=revision,
+                subfolder="",
+                _from_auto=False,
+                _from_pipeline=None,
+                **kwargs,
+            )
+        else:
+            model_kwargs = kwargs
+        
+        if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
+            try:
+                from .quantizer import init_model_weight_int4
+                from accelerate import init_empty_weights, dispatch_model, infer_auto_device_map
+                from accelerate.utils import CustomDtype
+                from accelerate.utils import get_balanced_memory
+            except ImportError:
+                raise ImportError(f"Needs import model weight init func to run quantize.") 
+            # Instantiate model.
+            init_contexts = [no_init_weights(_enable=True)]
+            init_contexts.append(init_empty_weights())
+            with ContextManagers(init_contexts):
+                model = cls(config)
+            
+            model_file = os.path.join(pretrained_model_name_or_path, 'pytorch_model.bin')
+            state_dict = torch.load(model_file, map_location="cpu") 
+            model.is_quantized = True
+                        
+            device_map = kwargs.pop("device_map", None)
+            torch_dtype = kwargs.pop("torch_dtype", None)
+            if device_map is not None:
+                kwargs = {"no_split_module_classes": model._no_split_modules}
+                target_dtype = CustomDtype.INT4
+                max_memory = get_balanced_memory(
+                    model,
+                    dtype=target_dtype,
+                    low_zero=(device_map == "balanced_low_0"),
+                    max_memory=None,
+                    **kwargs,
+                )
+                kwargs["max_memory"] = max_memory
+                device_map = infer_auto_device_map(model, dtype=target_dtype, **kwargs)
+            model = init_model_weight_int4(config, model, state_dict)
+            
+            # Set model in evaluation mode to deactivate DropOut modules by default
+            model.eval()
+            # If it is a model with generation capabilities, attempt to load the generation config
+            if model.can_generate():
+                try:
+                    model.generation_config = GenerationConfig.from_pretrained(
+                        pretrained_model_name_or_path,
+                        cache_dir=cache_dir,
+                        force_download=force_download,
+                        resume_download=False,
+                        proxies=None,
+                        local_files_only=local_files_only,
+                        token=token,
+                        revision=revision,
+                        subfolder="",
+                        _from_auto=False,
+                        _from_pipeline=None,
+                        **kwargs,
+                    )
+                except (OSError, TypeError):
+                    logger.info(
+                        "Generation config file not found, using a generation config created from the model config."
+                    )
+                    pass
+            
+            if device_map is not None:
+                dispatch_model(model, device_map=device_map)
+            
+            return model
+
+        return super(BaichuanForCausalLM, cls).from_pretrained(pretrained_model_name_or_path, *model_args, 
+                config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes, 
+                force_download=force_download, local_files_only=local_files_only, token=token, revision=revision, 
+                use_safetensors=use_safetensors, **kwargs)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = 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] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+        **kwargs,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        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,
+            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)
+        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)
+            softmax_normalizer = shift_logits.max(-1).values ** 2
+            z_loss = self.config.z_loss_weight * softmax_normalizer.mean()
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels) + z_loss
+
+        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 quantize(self, bits: int):
+        try:
+            from .quantizer import quantize_online
+        except ImportError:
+            raise ImportError(f"Needs QLinear to run quantize.")
+        return quantize_online(self, bits)
+        
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -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(
+            {
+                "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):
+        return tuple(
+            tuple(past_state.index_select(0, beam_idx) for past_state in layer_past)
+            for layer_past in past_key_values
+        )
+
+    def _build_chat_input(
+        self, tokenizer, messages: List[dict], max_new_tokens: int = 0
+    ):
+        max_new_tokens = max_new_tokens or self.generation_config.max_new_tokens
+        max_input_tokens = self.config.model_max_length - max_new_tokens
+        max_input_tokens = max(self.config.model_max_length // 2, max_input_tokens)
+        total_input, round_input = [], []
+        for i, message in enumerate(messages[::-1]):
+            content_tokens = tokenizer.encode(message["content"])
+            if message["role"] == "user":
+                round_input = (
+                    [self.generation_config.user_token_id]
+                    + content_tokens
+                    + round_input
+                )
+                if (
+                    total_input
+                    and len(total_input) + len(round_input) > max_input_tokens
+                ):
+                    break
+                else:
+                    total_input = round_input + total_input
+                    if len(total_input) >= max_input_tokens:
+                        break
+                    else:
+                        round_input = []
+            elif message["role"] == "assistant":
+                round_input = (
+                    [self.generation_config.assistant_token_id]
+                    + content_tokens
+                    + [self.generation_config.eos_token_id]
+                    + round_input
+                )
+            else:
+                raise ValueError(f"message role not supported yet: {message['role']}")
+        total_input = total_input[-max_input_tokens:]  # truncate left
+        total_input.append(self.generation_config.assistant_token_id)
+        total_input = torch.LongTensor([total_input]).to(self.device)
+        return total_input
+
+    def chat(self, tokenizer, messages: List[dict], stream=False,
+             generation_config: Optional[GenerationConfig]=None):
+        generation_config = generation_config or self.generation_config
+        input_ids = build_chat_input(self, tokenizer, messages, 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

Baichuan2-13B-Chat/ms_wrapper.py

@@ -0,0 +1,74 @@
+import os
+import torch
+from typing import Union, Dict, Any
+from modelscope.pipelines.builder import PIPELINES
+from modelscope.models.builder import MODELS
+from modelscope.utils.constant import Tasks
+from modelscope.pipelines.base import Pipeline
+from modelscope.outputs import OutputKeys
+from modelscope.pipelines.nlp.text_generation_pipeline import TextGenerationPipeline
+from modelscope.models.base import Model, TorchModel
+from modelscope.utils.logger import get_logger
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+from transformers.generation.utils import GenerationConfig
+
+@PIPELINES.register_module(Tasks.text_generation, module_name='Baichuan2-13B-chatbot-pipe')
+class Baichuan13BChatTextGenerationPipeline(TextGenerationPipeline):
+    def __init__(
+            self,
+            model: Union[Model, str],
+            *args,
+            **kwargs):
+        self.model = Baichuan13BChatTextGeneration(model) if isinstance(model, str) else model
+        super().__init__(model=model, **kwargs)
+    
+    def preprocess(self, inputs, **preprocess_params) -> Dict[str, Any]:
+        return inputs
+    
+    def _sanitize_parameters(self, **pipeline_parameters):
+        return {},pipeline_parameters,{}
+    
+    # define the forward pass
+    def forward(self, inputs: Dict, **forward_params) -> Dict[str, Any]:
+        output = {}
+        device = self.model.model.device
+        input_ids = self.model.tokenizer(inputs, return_tensors="pt").input_ids.to(device)
+        pred = self.model.model.generate(input_ids,**forward_params)
+        out = self.model.tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)
+        output['text'] = out
+        return output
+    
+    # format the outputs from pipeline
+    def postprocess(self, input, **kwargs) -> Dict[str, Any]:
+        return input
+    
+
+@MODELS.register_module(Tasks.text_generation, module_name='Baichuan2-13B-Chat')
+class Baichuan13BChatTextGeneration(TorchModel):
+    def __init__(self, model_dir=None, *args, **kwargs):
+        super().__init__(model_dir, *args, **kwargs)
+        self.logger = get_logger()
+        # loading tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+        self.model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True)
+        # self.model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto",trust_remote_code=True)
+        self.model.generation_config = GenerationConfig.from_pretrained(model_dir)
+        self.model = self.model.eval()
+    
+    def forward(self,input: Dict, *args, **kwargs) -> Dict[str, Any]:
+        output = {}
+        response = self.model.chat(self.tokenizer, input, *args, **kwargs)
+        history = input.copy()
+        history.append({'role': 'assistant', 'content': response})
+        return {OutputKeys.RESPONSE:response, OutputKeys.HISTORY: history}
+    
+    def quantize(self, bits: int):
+        self.model = self.model.quantize(bits)
+        return self
+    
+    def infer(self, input, **kwargs):
+        device = self.model.device
+        input_ids = self.tokenizer(input, return_tensors="pt").input_ids.to(device)
+        pred = self.model.generate(input_ids,**kwargs)
+        out = self.tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)
+        return out

Baichuan2-13B-Chat/pytorch_model-00001-of-00003.bin

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+oid sha256:ed5f863097bc541ff1f47f5d5ac875cbac2c1269c384bc77f540ffca4cc2cb5d
+size 9973568983

Baichuan2-13B-Chat/pytorch_model-00002-of-00003.bin

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+oid sha256:13ac578202dce2688d81fcee55e332d898ad980cec5019a7de386ea9760302c0
+size 9947421360

Baichuan2-13B-Chat/pytorch_model-00003-of-00003.bin

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+size 7872446643

Baichuan2-13B-Chat/pytorch_model.bin.index.json

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+    "model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
+    "model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
+    "model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
+    "model.layers.9.self_attn.W_pack.weight": "pytorch_model-00001-of-00003.bin",
+    "model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
+    "model.norm.weight": "pytorch_model-00003-of-00003.bin"
+  }
+}

Baichuan2-13B-Chat/quantizer.py

@@ -0,0 +1,215 @@
+try:
+    import bitsandbytes as bnb
+    from bitsandbytes.nn.modules import Params4bit, Int8Params
+except ImportError:
+    print('import bitsandbytes Error')
+
+from accelerate import init_empty_weights
+import torch 
+
+def Params4bitCuda(self, device):
+    self.data = self.data.cuda(device)
+    self.quant_state[0] = self.quant_state[0].cuda(device)
+    self.quant_state[4][0] = self.quant_state[4][0].cuda(device)
+    self.quant_state[4][1][0] = self.quant_state[4][1][0].cuda(device)
+    self.quant_state[4][1][1] = self.quant_state[4][1][1].cuda(device)
+
+    self.quant_state[6] = self.quant_state[6].cuda(device)
+    return self
+
+class Linear4bitOnline(torch.nn.Module):
+    def __init__(self, weight, bias, quant_type):
+        super().__init__()
+        self.weight = Params4bit(
+            weight.data, requires_grad=False, compress_statistics=True, quant_type=quant_type
+        )
+        self.compute_dtype = None
+        #self.weight.cuda(weight.device)
+        self.bias = bias
+
+    def forward(self, x: torch.Tensor):
+        # weights are cast automatically as Int8Params, but the bias has to be cast manually
+        if self.bias is not None and self.bias.dtype != x.dtype:
+            self.bias.data = self.bias.data.to(x.dtype)
+
+        if getattr(self.weight, "quant_state", None) is None:
+            print(
+                "FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first."
+            )
+        inp_dtype = x.dtype
+        if self.compute_dtype is not None:
+            x = x.to(self.compute_dtype)
+
+        bias = None if self.bias is None else self.bias.to(self.compute_dtype)
+        out = bnb.matmul_4bit(
+            x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state
+        )
+
+        out = out.to(inp_dtype)
+
+        return out
+    
+class Linear8bitLtOnline(torch.nn.Module):
+    def __init__(
+        self,
+        weight,
+        bias,
+        has_fp16_weights=True,
+        memory_efficient_backward=False,
+        threshold=0.0,
+        index=None,
+    ):
+        super().__init__()
+        assert (
+            not memory_efficient_backward
+        ), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
+        self.state = bnb.MatmulLtState()
+        self.index = index
+
+        # Necessary for stacked layers
+        self.state.threshold = threshold
+        self.state.has_fp16_weights = has_fp16_weights
+        self.state.memory_efficient_backward = memory_efficient_backward
+        if threshold > 0.0 and not has_fp16_weights:
+            self.state.use_pool = True
+
+        self.weight = Int8Params(
+            weight.data,
+            has_fp16_weights=has_fp16_weights,
+            requires_grad=has_fp16_weights,
+        )
+        self.bias = bias
+
+    def init_8bit_state(self):
+        self.state.CB = self.weight.CB
+        self.state.SCB = self.weight.SCB
+        self.weight.CB = None
+        self.weight.SCB = None
+
+    def forward(self, x: torch.Tensor):
+        self.state.is_training = self.training
+        if self.weight.CB is not None:
+            self.init_8bit_state()
+
+        # weights are cast automatically as Int8Params, but the bias has to be cast manually
+        if self.bias is not None and self.bias.dtype != x.dtype:
+            self.bias.data = self.bias.data.to(x.dtype)
+        
+        out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
+
+        if not self.state.has_fp16_weights:
+            if self.state.CB is not None and self.state.CxB is not None:
+                # we converted 8-bit row major to turing/ampere format in the first inference pass
+                # we no longer need the row-major weight
+                del self.state.CB
+                self.weight.data = self.state.CxB
+        return out
+    
+def quantize_offline(model, bits: int):
+    assert (bits == 4), f'bits: {bits} is not supported'
+    
+    for i, layer in enumerate(model.model.layers):
+        layer.self_attn.W_pack = bnb.nn.Linear4bit(
+                            layer.self_attn.W_pack.weight.shape[1],
+                            layer.self_attn.W_pack.weight.shape[0],
+                            False,
+                            torch.float16,
+                            compress_statistics=True,
+                            quant_type="nf4",
+                        )
+        layer.self_attn.o_proj = bnb.nn.Linear4bit(
+                            layer.self_attn.o_proj.weight.shape[1],
+                            layer.self_attn.o_proj.weight.shape[0],
+                            False,
+                            torch.float16,
+                            compress_statistics=True,
+                            quant_type="nf4",
+                        )
+
+        layer.mlp.gate_proj = bnb.nn.Linear4bit(
+                            layer.mlp.gate_proj.weight.shape[1],
+                            layer.mlp.gate_proj.weight.shape[0],
+                            False,
+                            torch.float16,
+                            compress_statistics=True,
+                            quant_type="nf4",
+                        )
+        layer.mlp.down_proj = bnb.nn.Linear4bit(
+                            layer.mlp.down_proj.weight.shape[1],
+                            layer.mlp.down_proj.weight.shape[0],
+                            False,
+                            torch.float16,
+                            compress_statistics=True,
+                            quant_type="nf4",
+                        )
+        layer.mlp.up_proj = bnb.nn.Linear4bit(
+                            layer.mlp.up_proj.weight.shape[1],
+                            layer.mlp.up_proj.weight.shape[0],
+                            False,
+                            torch.float16,
+                            compress_statistics=True,
+                            quant_type="nf4",
+                        )
+    return model
+
+def quantize_online(model, bits: int):
+    def quant(weight, bias=None):
+        if bits == 8:
+            linear = Linear8bitLtOnline(
+                weight,
+                bias,
+                has_fp16_weights=False,
+                threshold=6.0,
+            )
+            if bias is not None:
+                linear.bias = torch.nn.Parameter(bias)
+        elif bits == 4:
+            linear = Linear4bitOnline(
+                weight,
+                bias,
+                quant_type="nf4", #fp4/nf4
+            )
+        else:
+            raise ValueError("quantize only support 4/8 bit")
+        return linear
+
+    for i, layer in enumerate(model.model.layers):
+        layer.self_attn.W_pack = quant(layer.self_attn.W_pack.weight)
+        layer.self_attn.o_proj = quant(layer.self_attn.o_proj.weight)
+        layer.mlp.gate_proj = quant(layer.mlp.gate_proj.weight)
+        layer.mlp.down_proj = quant(layer.mlp.down_proj.weight)
+        layer.mlp.up_proj = quant(layer.mlp.up_proj.weight)
+    return model
+
+def init_model_weight_int4(config, model, state_dict):
+    #replace Params4bit.cuda with Params4bitCuda
+    Params4bit.cuda = Params4bitCuda
+
+    for i in range(config.num_hidden_layers):
+        weight_data = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.data']
+        weight_quant_state = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.quant_state']
+        model.model.layers[i].self_attn.W_pack.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
+        
+        weight_data = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.data']
+        weight_quant_state = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.quant_state']
+        model.model.layers[i].self_attn.o_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
+        
+        weight_data = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.data']
+        weight_quant_state = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.quant_state']
+        model.model.layers[i].mlp.gate_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
+        
+        weight_data = state_dict[f'model.layers.{i}.mlp.up_proj.weight.data']
+        weight_quant_state = state_dict[f'model.layers.{i}.mlp.up_proj.weight.quant_state']
+        model.model.layers[i].mlp.up_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
+        
+        weight_data = state_dict[f'model.layers.{i}.mlp.down_proj.weight.data']
+        weight_quant_state = state_dict[f'model.layers.{i}.mlp.down_proj.weight.quant_state']
+        model.model.layers[i].mlp.down_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
+        
+        model.model.layers[i].input_layernorm.weight = state_dict[f'model.layers.{i}.input_layernorm.weight']
+        model.model.layers[i].post_attention_layernorm.weight = state_dict[f'model.layers.{i}.post_attention_layernorm.weight']
+    
+    model.model.embed_tokens.weight = state_dict['model.embed_tokens.weight']
+    model.model.norm.weight = state_dict['model.norm.weight']
+    model.lm_head.weight = state_dict['lm_head.weight'] 
+    return model

Baichuan2-13B-Chat/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": true
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": true
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": true
+  },
+  "pad_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": true
+  }
+}

Baichuan2-13B-Chat/tokenization_baichuan.py

@@ -0,0 +1,258 @@
+# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
+
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {},
+    "tokenizer_file": {},
+}
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
+
+
+class BaichuanTokenizer(PreTrainedTokenizer):
+    """
+    Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+
+    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,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token=None,
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_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.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            sp_model_kwargs=self.sp_model_kwargs,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(self.vocab_file)
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text):
+        """Returns a tokenized string."""
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for i, token in enumerate(tokens):
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special and i != 0:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string
+
+    def save_vocabulary(
+        self, save_directory, filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "")
+            + VOCAB_FILES_NAMES["vocab_file"],
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(
+            out_vocab_file
+        ) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = bos_token_id + token_ids_0 + eos_token_id
+
+        if token_ids_1 is not None:
+            output = output + bos_token_id + token_ids_1 + eos_token_id
+
+        return output
+
+    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]:
+        """
+        Retrieve 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` method.
+
+        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,
+            )
+
+        bos_token_id = [1] if self.add_bos_token else []
+        eos_token_id = [1] if self.add_eos_token else []
+
+        if token_ids_1 is None:
+            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
+        return (
+            bos_token_id
+            + ([0] * len(token_ids_0))
+            + eos_token_id
+            + bos_token_id
+            + ([0] * len(token_ids_1))
+            + eos_token_id
+        )
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
+        sequence pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        if token_ids_1 is None, only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of ids.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+        """
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
+
+        if token_ids_1 is not None:
+            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
+
+        return output

Baichuan2-13B-Chat/tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79452955be6b419a65984273a9f08af86042e1c2a75ee3ba989cbf620a133cc2
+size 2001107

Baichuan2-13B-Chat/tokenizer_config.json

@@ -0,0 +1,46 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_baichuan.BaichuanTokenizer",
+      null
+    ]
+  },
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": true
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": true
+  },
+  "model_max_length": 4096,
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": true
+  },
+  "sp_model_kwargs": {},
+  "tokenizer_class": "BaichuanTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": true
+  }
+}

Baichuan2-13B-Chat/upload.py

@@ -0,0 +1,10 @@
+from modelscope.hub.api import HubApi
+
+YOUR_ACCESS_TOKEN = 'f1775e28-a401-4ad3-9442-9903fbfb80dd'
+
+api = HubApi()
+api.login(YOUR_ACCESS_TOKEN)
+api.push_model(
+    model_id="V1ncentS/RC-GPT",
+    model_dir="D:\复旦\金融风控LLM+KG\Baichuan\Baichuan2-13B-Chat"  # 本地模型目录，要求目录中必须包含configuration.json
+)