增加lmdeploy测试config

configs/eval_llama2_chat_lmdelpoy.py

+from mmengine.config import read_base
+from opencompass.models.turbomind import TurboMindModel
+
+with read_base():
+    from .datasets.ARC_c.ARC_c_gen_1e0de5 import ARC_c_datasets 
+    from .datasets.ARC_e.ARC_e_gen_1e0de5 import ARC_e_datasets
+    from .summarizers.example import summarizer
+    
+datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
+work_dir = './outputs/llama2-chat/'
+
+llama_chat_meta_template = dict(
+    round=[
+        dict(role="HUMAN", begin='[INST] ', end=' [/INST]'),
+        dict(role="BOT", begin=' ', end=' ', generate=True),
+    ],
+)
+
+models = [
+dict(
+        type=TurboMindModel,
+        abbr='llama-2-7b-chat-hf-lmdeploy',
+        path="Llama-2-7b-chat-hf",
+        meta_template=llama_chat_meta_template,
+        engine_config=dict(session_len=4096,
+                           max_batch_size=32),
+        gen_config=dict(top_k=1,
+                        top_p=0.8,
+                        temperature=1.0,
+                        max_new_tokens=100),
+        max_out_len=100,
+        max_seq_len=2048,
+        batch_size=1,
+        concurrency=1,
+        run_cfg=dict(num_gpus=1, num_procs=1),
+        end_str='[INST]',
+    )
+]
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configs/eval_qwen1.5_chat_vllm.py

@@ -12,7 +12,7 @@ work_dir = './outputs/qwen1.5-chat/'
 from opencompass.models import VLLM
 
 
-_meta_template = dict(
+qwen_meta_template = dict(
     round=[
         dict(role="HUMAN", begin='<|im_start|>user\n', end='<|im_end|>\n'),
         dict(role="BOT", begin="<|im_start|>assistant\n", end='<|im_end|>\n', generate=True),
@@ -26,7 +26,7 @@ models = [
         abbr='qwen1.5-7b-chat-vllm',
         path="Qwen1.5-7B-Chat",
         model_kwargs=dict(tensor_parallel_size=1, enforce_eager=True, dtype="float16"),
-        meta_template=_meta_template,
+        meta_template=qwen_meta_template,
         max_out_len=100,
         max_seq_len=2048,
         batch_size=1,

configs/eval_qwen_chat_lmdeploy.py

+from mmengine.config import read_base
+from opencompass.models.turbomind import TurboMindModel
+
+with read_base():
+    from .datasets.ARC_c.ARC_c_gen_1e0de5 import ARC_c_datasets 
+    from .datasets.ARC_e.ARC_e_gen_1e0de5 import ARC_e_datasets
+    from .summarizers.example import summarizer
+    
+datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
+work_dir = './outputs/qwen-chat/'
+
+qwen_meta_template = dict(
+    round=[
+        dict(role="HUMAN", begin='\n<|im_start|>user\n', end='<|im_end|>'),
+        dict(role="BOT", begin="\n<|im_start|>assistant\n", end='<|im_end|>', generate=True),
+    ],
+)
+models = [
+dict(
+        type=TurboMindModel,
+        abbr='qwen-7b-lmdeploy',
+        path="./Qwen-7B-chat",
+        meta_template=qwen_meta_template,
+        engine_config=dict(session_len=8192,
+                           max_batch_size=32
+                           ),
+        gen_config=dict(top_k=1,
+                        top_p=0.8,
+                        temperature=1.0,
+                        max_new_tokens=100),
+        max_out_len=100,
+        max_seq_len=2048,
+        batch_size=1,
+        concurrency=1,
+        run_cfg=dict(num_gpus=1, num_procs=1),
+        end_str='[INST]'
+    )
+]
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configs/eval_qwen_lmdeploy.py

+from mmengine.config import read_base
+from opencompass.models.turbomind import TurboMindModel
+
+with read_base():
+    from .datasets.ARC_c.ARC_c_gen_1e0de5 import ARC_c_datasets 
+    from .datasets.ARC_e.ARC_e_gen_1e0de5 import ARC_e_datasets
+    from .summarizers.example import summarizer
+    
+datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
+work_dir = './outputs/qwen/'
+
+models = [
+dict(
+        type=TurboMindModel,
+        abbr='qwen-7b-lmdeploy',
+        path="/Qwen-7B",
+        engine_config=dict(session_len=8192,
+                           max_batch_size=32),
+        gen_config=dict(top_k=1,
+                        top_p=0.8,
+                        temperature=1.0,
+                        max_new_tokens=100),
+        max_out_len=100,
+        max_seq_len=2048,
+        batch_size=1,
+        concurrency=1,
+        run_cfg=dict(num_gpus=1, num_procs=1)
+    )
+]
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