update vllm configs

configs/eval_chatglm3_vllm.py

@@ -15,7 +15,7 @@ models = [
         type=VLLM,
         abbr='chatglm3-6b-32k-vllm',
         path='chatglm3-6b-32k',
-        model_kwargs=dict(tensor_parallel_size=1, enforce_eager=True, dtype="float16"),
+        model_kwargs=dict(tensor_parallel_size=1),
         max_out_len=100,
         max_seq_len=4096,
         batch_size=1,

configs/eval_llama2_chat_vllm.py

@@ -11,7 +11,7 @@ work_dir = './outputs/llama2-chat/'
 from opencompass.models import VLLM
 
 
-llama_meta_template = dict(
+llama2_meta_template = dict(
     round=[
         dict(role="HUMAN", begin='[INST] ', end=' [/INST]'),
         dict(role="BOT", begin=' ', end=' ', generate=True),
@@ -23,8 +23,8 @@ models = [
         type=VLLM,
         abbr='llama-2-7b-chat-vllm',
         path="Llama-2-7b-chat-hf",
-        model_kwargs=dict(tensor_parallel_size=1, enforce_eager=True, dtype="float16"),
-        meta_template=llama_meta_template,
+        model_kwargs=dict(tensor_parallel_size=1),
+        meta_template=llama2_meta_template,
         max_out_len=100,
         max_seq_len=2048,
         batch_size=1,

configs/eval_llama2_int4_chat_vllm.py

@@ -23,7 +23,7 @@ models = [
         type=VLLM,
         abbr='llama-2-7b-chat-vllm',
         path="Llama-2-7B-Chat-GPTQ",
-        model_kwargs=dict(tensor_parallel_size=1, enforce_eager=True, dtype="float16", quantization="gptq"),
+        model_kwargs=dict(tensor_parallel_size=1, quantization="gptq"),
         meta_template=llama_meta_template,
         max_out_len=100,
         max_seq_len=2048,

configs/eval_llama2_vllm.py

@@ -15,7 +15,7 @@ models = [
         type=VLLM,
         abbr='llama-2-7b-vllm',
         path="Llama-2-7b-hf",
-        model_kwargs=dict(tensor_parallel_size=1, enforce_eager=True, dtype="float16"),
+        model_kwargs=dict(tensor_parallel_size=1),
         max_out_len=100,
         max_seq_len=2048,
         batch_size=1,

configs/eval_llama3_instruct_vllm.py

+from mmengine.config import read_base
+
+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") or k == 'datasets'], [])
+work_dir = './outputs/llama3-instruct/'
+
+from opencompass.models import VLLM
+
+
+llama3_meta_template = dict(
+    round=[
+        dict(role="HUMAN", begin="<|begin_of_text|>user<|end_header_id|>\n\n", end="<|eot_id|>"),
+        dict(role="BOT", begin="<|begin_of_text|>assistant<|end_header_id|>\n\n", end="<|eot_id|>", generate=True),
+    ],
+    eos_token_id=[128001, 128009],
+)
+
+models = [
+    dict(
+        type=VLLM,
+        abbr="llama-3-8b-instruct-hf",
+        path="Meta-Llama-3-8B-Instruct",
+        model_kwargs=dict(tensor_parallel_size=1),
+        meta_template=llama3_meta_template,
+        max_out_len=100,
+        max_seq_len=2048,
+        batch_size=1,
+        generation_kwargs=dict(temperature=0),
+        run_cfg=dict(num_gpus=1, num_procs=1),  
+    )
+]
\ No newline at end of file

configs/eval_qwen1.5_chat_vllm.py

@@ -12,7 +12,7 @@ work_dir = './outputs/qwen1.5-chat/'
 from opencompass.models import VLLM
 
 
-qwen_meta_template = dict(
+qwen2_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),
@@ -25,8 +25,8 @@ models = [
         type=VLLM,
         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=qwen_meta_template,
+        model_kwargs=dict(tensor_parallel_size=1),
+        meta_template=qwen2_meta_template,
         max_out_len=100,
         max_seq_len=2048,
         batch_size=1,

configs/eval_qwen1.5_int4_chat_vllm.py

@@ -12,7 +12,7 @@ work_dir = './outputs/qwen1.5-int4-chat/'
 from opencompass.models import VLLM
 
 
-qwen_meta_template = dict(
+qwen2_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),
@@ -25,8 +25,8 @@ models = [
         type=VLLM,
         abbr='qwen1.5-7b-int4-chat-vllm',
         path="Qwen1.5-7B-Chat-GPTQ-Int4",
-        model_kwargs=dict(tensor_parallel_size=1, enforce_eager=True, dtype="float16", quantization="gptq"),
-        meta_template=qwen_meta_template,
+        model_kwargs=dict(tensor_parallel_size=1, quantization="gptq"),
+        meta_template=qwen2_meta_template,
         max_out_len=100,
         max_seq_len=2048,
         batch_size=1,

configs/eval_qwen1.5_vllm.py

@@ -16,7 +16,7 @@ models = [
         type=VLLM,
         abbr='qwen1.5-7b-vllm',
         path="Qwen1.5-7B",
-        model_kwargs=dict(tensor_parallel_size=1, enforce_eager=True, dtype="float16"),
+        model_kwargs=dict(tensor_parallel_size=1),
         max_out_len=100,
         max_seq_len=2048,
         batch_size=1,

configs/eval_qwen_chat_vllm.py

@@ -23,7 +23,7 @@ models = [
         type=VLLM,
         abbr='qwen-7b-chat-vllm',
         path="Qwen-7B-Chat",
-        model_kwargs=dict(tensor_parallel_size=2, enforce_eager=True, dtype="float16"),
+        model_kwargs=dict(tensor_parallel_size=2),
         meta_template=qwen_meta_template,
         max_out_len=100,
         max_seq_len=2048,

configs/eval_qwen_int4_chat.py

@@ -23,7 +23,7 @@ models = [
         type=VLLM,
         abbr='qwen-7b-int4-chat-vllm',
         path="Qwen-7B-Chat-GPTQ-Int4",
-        model_kwargs=dict(tensor_parallel_size=2, enforce_eager=True, dtype="float16", quantization="gptq"),
+        model_kwargs=dict(tensor_parallel_size=2, quantization="gptq"),
         meta_template=qwen_meta_template,
         max_out_len=100,
         max_seq_len=2048,

opencompass/models/vllm.py

@@ -8,7 +8,7 @@ try:
 except ImportError:
     LLM, SamplingParams = None, None
 
-DEFAULT_MODEL_KWARGS = dict(trust_remote_code=True)
+DEFAULT_MODEL_KWARGS = dict(trust_remote_code=True, enforce_eager=True, dtype="float16")
 
 
 class VLLM(BaseModel):