### 修改部分(qwen1.5)
1.requirements/runtime.txt transformers==4.38.2<br>
2.lmdeploy/turbomind/deploy/source_model/qwen.py <br>
要将文件内容变成下面的 添加qwen的模型权重读取对应<br>
```python
# Copyright (c) OpenMMLab. All rights reserved.
import jsonz
import os.path as osp

import torch

from .base import INPUT_MODELS
from .llama import LlamaModel, LlamaReader


class QwenReader(LlamaReader):
    """QwenReader."""

    attn_layer_patten = r'transformer.h.([0-9]+).'
    tok_embeddings_key = 'transformer.wte.weight'
    norm_weight_key = 'transformer.ln_f.weight'
    output_weight_key = 'lm_head.weight'

    def __init__(self, new_params: dict, unused_params: dict, last_bin: bool):
        super().__init__(new_params, unused_params, last_bin)

    def _attn(self, i: int, kind: str, size_dim: int, dim: int = 0):
        """Get q, k, v, o kind for layer i."""
        qkv = self.params[f'transformer.h.{i}.attn.c_attn.{kind}']
        q, k, v = torch.split(qkv, qkv.size(size_dim) // 3, dim=dim)
        o = self.params.get(f'transformer.h.{i}.attn.c_proj.{kind}', None)
        if o is None:
            o = torch.zeros_like(q)
        return q, k, v, o

    def attn(self, i: int):
        """Get q, k, v, o weight for layer i."""
        return self._attn(i, 'weight', 0, 0)

    def attn_bias(self, i: int):
        """Get q, k, v, o bias for layer i."""
        return self._attn(i, 'bias', -1, 0)

    def attn_zero(self, i: int):
        """Get q, k, v, o zero point for layer i."""
        return (None, ) * 4

    def attn_scale(self, i: int):
        """Get q, k, v, o scale for layer i."""
        return (None, ) * 4

    def attn_norm(self, i: int):
        """Get attn norm for layer i."""
        return self.params[f'transformer.h.{i}.ln_1.weight']

    def _ffn(self, i: int, kind: str):
        """Get ffn kind for layer i."""
        result = []
        for key in ['w2', 'c_proj', 'w1']:
            tensor = self.params[f'transformer.h.{i}.mlp.{key}.{kind}']
            result.append(tensor)
        return (*result, )

    def ffn(self, i: int):
        """Get ffn weight for layer i."""
        return self._ffn(i, 'weight')

    def ffn_zero(self, i: int):
        """Get ffn zero point for layer i."""
        return (None, ) * 3

    def ffn_scale(self, i: int):
        """Get ffn scale for layer i."""
        return (None, ) * 3

    def ffn_norm(self, i: int):
        """Get ffn norm for layer i."""
        return self.params[f'transformer.h.{i}.ln_2.weight']


@INPUT_MODELS.register_module(name='qwen')
class QwenModel(LlamaModel):
    """Qwen model in hf format."""

    Reader = QwenReader

    def __init__(self, model_path: str, tokenizer_path: str, **kwargs):
        super().__init__(model_path, tokenizer_path, **kwargs)

    def tokenizer_info(self):
        """Read tokenizer info."""
        n_words = 151851
        bos_id = 0
        eos_id = 151643
        return n_words, bos_id, eos_id

    def model_info(self):
        """Read model info."""
        params_path = osp.join(self.model_path, 'config.json')
        with open(params_path) as f:
            config = json.load(f)
            num_layer = config['num_hidden_layers']
            norm_eps = config['layer_norm_epsilon']
            rope_theta = float(config.get('rotary_emb_base', 10000.0))
            if 'num_key_value_heads' in config:
                kv_head_num = config['num_key_value_heads']
            else:
                kv_head_num = config['num_attention_heads']
            seq_length = config['seq_length']
            use_dynamic_ntk = int(config['use_dynamic_ntk'])
            use_logn_attn = int(config['use_logn_attn'])
        return dict(num_layer=num_layer,
                    norm_eps=norm_eps,
                    kv_head_num=kv_head_num,
                    rope_theta=rope_theta,
                    max_position_embeddings=seq_length,
                    use_dynamic_ntk=int(use_dynamic_ntk),
                    use_logn_attn=use_logn_attn)
class Qwen2Reader(LlamaReader):
    """read qwen2 model weights.

    The weight name of qwen2 model is similar to llama, except its attention
    bias doesn't include o_proj bias. Therefore, we make a dummy zero o_proj
    bias to make it comply the definition of turbomind llama format
    """

    def __init__(self, new_params: dict, unused_params: dict, last_bin: bool):
        super().__init__(new_params, unused_params, last_bin)

    def attn_bias(self, i: int):
        """Get q, k, v bias for layer i."""
        result = []

        for key in ['q', 'k', 'v']:
            tensor = self.params.get(
                f'model.layers.{i}.self_attn.{key}_proj.bias')
            assert tensor is not None
            result.append(tensor)

        tensor = self.params.get(f'model.layers.{i}.self_attn.o_proj.weight')
        dummy_oproj_bias = tensor.new_zeros(tensor.shape[0])
        result.append(dummy_oproj_bias)
        return (*result, )


@INPUT_MODELS.register_module(name='qwen2')
class Qwen2Model(LlamaModel):
    """Qwen model in hf format."""

    Reader = Qwen2Reader

    def __init__(self, model_path: str, tokenizer_path: str, **kwargs):
        super().__init__(model_path, tokenizer_path, **kwargs)

    def tokenizer_info(self):
        """set tokenizer info.

        Refer to https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/generation_config.json
        """  # noqa: E501
        n_words = 152064
        bos_id = 151643
        eos_id = 151645
        return n_words, bos_id, eos_id

```
3.lmdeploy/turbomind/deploy/converter.py<br>
修改下面的部分添加qwen2 对应<br>
```python
supported_formats = ['llama', 'hf', 'awq', None]
special_input_model_map = {
    'qwen2':'qwen2',
    'qwen': 'qwen',
    'baichuan': 'baichuan',
    'baichuan2': 'baichuan2'
}


def get_package_root_path():
    """Get lmdeploy root path."""
    import lmdeploy
```
4.lmdeploy/model.py<br>
修改下面的代码可以添加qwen2 的prompt
```python
@MODELS.register_module(name='qwen2-110b')
@MODELS.register_module(name='qwen2-72b')
@MODELS.register_module(name='qwen2-14b')
@MODELS.register_module(name='qwen2-7b')
@MODELS.register_module(name='qwen-72b')
@MODELS.register_module(name='qwen-14b')
@MODELS.register_module(name='qwen-7b')
class Qwen7BChat(BaseModel):
    """Chat template for Qwen-7B-Chat."""

    def __init__(self,
                 session_len=8192,
                 top_p=0.5,
                 top_k=40,
```