fix bugs (#1038)

Author: Jintao-Huang

docs/source/LLM/VLLM推理加速与部署.md

@@ -18,7 +18,7 @@ pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
 pip install 'ms-swift[llm]' -U
 
 # vllm与cuda版本有对应关系，请按照`https://docs.vllm.ai/en/latest/getting_started/installation.html`选择版本
-pip install vllm -U
+pip install vllm
 pip install openai -U
 
 # 环境对齐 (通常不需要运行. 如果你运行错误, 可以跑下面的代码, 仓库使用最新环境测试)
@@ -484,7 +484,7 @@ CUDA_VISIBLE_DEVICES=0 swift deploy --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx-merged
 
 客户端示例代码同原始模型.
 
-### 多LoRA部署
+## 多LoRA部署
 
 目前pt方式部署模型已经支持`peft>=0.10.0`进行多LoRA部署，具体方法为：
 

docs/source_en/LLM/VLLM-inference-acceleration-and-deployment.md

@@ -15,7 +15,7 @@ pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
 pip install 'ms-swift[llm]' -U
 
 # vllm version corresponds to cuda version, please select version according to `https://docs.vllm.ai/en/latest/getting_started/installation.html`
-pip install vllm -U
+pip install vllm
 pip install openai -U
 
 # Environment alignment (usually not needed. If you get errors, you can run the code below, the repo uses the latest environment for testing)
@@ -481,7 +481,7 @@ CUDA_VISIBLE_DEVICES=0 swift deploy --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx-merged
 
 The example code for the client side is the same as the original models.
 
-### Multiple LoRA Deployments
+## Multiple LoRA Deployments
 
 The current model deployment method now supports multiple LoRA deployments with `peft>=0.10.0`. The specific steps are:
 

examples/pytorch/llm/custom.py

@@ -28,11 +28,11 @@ class CustomDatasetName:
     stsb_en = 'stsb-en'
 
 
-@register_model(CustomModelType.tigerbot_7b, 'TigerResearch/tigerbot-7b-base-v3', LoRATM.llama,
+@register_model(CustomModelType.tigerbot_7b, 'TigerResearch/tigerbot-7b-base-v3', LoRATM.llama2,
                 TemplateType.default_generation)
-@register_model(CustomModelType.tigerbot_13b, 'TigerResearch/tigerbot-13b-base-v2', LoRATM.llama,
+@register_model(CustomModelType.tigerbot_13b, 'TigerResearch/tigerbot-13b-base-v2', LoRATM.llama2,
                 TemplateType.default_generation)
-@register_model(CustomModelType.tigerbot_13b_chat, 'TigerResearch/tigerbot-13b-chat-v4', LoRATM.llama,
+@register_model(CustomModelType.tigerbot_13b_chat, 'TigerResearch/tigerbot-13b-chat-v4', LoRATM.llama2,
                 CustomTemplateType.tigerbot)
 def get_tigerbot_model_tokenizer(model_dir: str,
                                  torch_dtype: Dtype,

swift/llm/deploy.py

@@ -42,7 +42,7 @@ async def get_available_models():
     if _args.lora_request_list is not None:
         model_list += [lora_request.lora_name for lora_request in _args.lora_request_list]
     data = [
-        Model(id=model_id, is_chat=not is_generation_template(model_id), owned_by=_args.owned_by)
+        Model(id=model_id, is_chat=not is_generation_template(_args.template_type), owned_by=_args.owned_by)
         for model_id in model_list
     ]
     return ModelList(data=data)

swift/llm/utils/argument.py

@@ -220,6 +220,8 @@ def handle_compatibility(self: Union['SftArguments', 'InferArguments']) -> None:
             _dataset = getattr(self, key)
             if isinstance(_dataset, str):
                 _dataset = [_dataset]
+            elif _dataset is None:
+                _dataset = []
             if len(_dataset) == 1 and ',' in _dataset[0]:
                 _dataset = _dataset[0].split(',')
             for i, d in enumerate(_dataset):

swift/llm/utils/model.py

@@ -435,6 +435,8 @@ class LoRATM(NamedTuple):
         'kv_b_proj',
         'o_proj',
     ]
+    # compat
+    llama2 = llama
 
 
 GetModelTokenizerFunction = Callable[..., Tuple[Optional[PreTrainedModel], PreTrainedTokenizerBase]]