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vllm configs and added question to reference in prediction outputs
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,10 +1,15 @@ | ||
| from mmengine.config import read_base | ||
| with read_base(): | ||
| from ..model_template import get_default_model | ||
| from ..model_template import get_default_model, get_vllm_model | ||
| from ...paths import ROOT_DIR | ||
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| vicuna_7b_v1_5 = get_default_model(abbr="vicuna-7b-v1.5", path=f"{ROOT_DIR}models/lmsys/vicuna-7b-v1.5") | ||
| vicuna_13b_v1_5 = get_default_model(abbr="vicuna-13b-v1.5", path=f"{ROOT_DIR}models/lmsys/vicuna-13b-v1.5") | ||
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| vicuna_bases = [vicuna_7b_v1_5, vicuna_13b_v1_5] | ||
| vicuna_bases = [vicuna_7b_v1_5, vicuna_13b_v1_5] | ||
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| vicuna_7b_v1_5_vllm = get_vllm_model(abbr="vicuna-7b-v1.5", path=f"{ROOT_DIR}models/lmsys/vicuna-7b-v1.5") | ||
| vicuna_13b_v1_5_vllm = get_vllm_model(abbr="vicuna-13b-v1.5", path=f"{ROOT_DIR}models/lmsys/vicuna-13b-v1.5") | ||
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| vicuna_bases_vllm = [vicuna_7b_v1_5_vllm, vicuna_13b_v1_5_vllm] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,8 @@ | ||
| from mmengine.config import read_base | ||
| with read_base(): | ||
| from ..model_template import get_default_model | ||
| from ..model_template import get_default_model, get_vllm_model | ||
| from ...paths import ROOT_DIR | ||
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| mistral_7b = get_default_model(abbr="mistral-7b", path=f"{ROOT_DIR}models/mistralai/Mistral-7B-v0.1") | ||
| mistral_7b = get_default_model(abbr="mistral-7b", path=f"{ROOT_DIR}models/mistralai/Mistral-7B-v0.1") | ||
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| mistral_7b_vllm = get_vllm_model(abbr="mistral-7b", path=f"{ROOT_DIR}models/mistralai/Mistral-7B-v0.1") |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,17 +1,42 @@ | ||
| from mmengine.config import read_base | ||
| with read_base(): | ||
| from ...paths import ROOT_DIR | ||
| from ..model_template import get_default_model | ||
| from ..model_template import get_default_model, get_vllm_model | ||
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| yi_meta_template = dict( | ||
| round=[ | ||
| dict(role="HUMAN", begin='<|im_start|>user\n', end='<|im_end|>'), | ||
| dict(role="BOT", begin="<|im_start|>assistant\n", end='<|im_end|>', generate=True), | ||
| ], | ||
| ) | ||
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| yi_6b = get_default_model(abbr="yi-6b", path=f"{ROOT_DIR}models/01-ai/Yi-6B") | ||
| yi_9b = get_default_model(abbr="yi-9b", path=f"{ROOT_DIR}models/01-ai/Yi-9B") | ||
| yi_34b = get_default_model(abbr="yi-34b", path=f"{ROOT_DIR}models/01-ai/Yi-34B") | ||
| yi_34b = get_default_model(abbr="yi-34b", path=f"{ROOT_DIR}models/01-ai/Yi-34B", num_gpus=2) | ||
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| yi_6b_chat = get_default_model(abbr="yi-6b-chat", path=f"{ROOT_DIR}models/01-ai/Yi-6B-Chat") | ||
| yi_9b_chat = get_default_model(abbr="yi-9b-chat", path=f"{ROOT_DIR}models/01-ai/Yi-9B-Chat") | ||
| yi_34b_chat = get_default_model(abbr="yi-34b-chat", path=f"{ROOT_DIR}models/01-ai/Yi-34B-Chat") | ||
| yi_6b_chat = get_default_model(abbr="yi-6b-chat", path=f"{ROOT_DIR}models/01-ai/Yi-6B-Chat", meta_template=yi_meta_template) | ||
| # yi_9b_chat = get_default_model(abbr="yi-9b-chat", path=f"{ROOT_DIR}models/01-ai/Yi-9B-Chat", meta_template=yi_meta_template) | ||
| yi_34b_chat = get_default_model(abbr="yi-34b-chat", path=f"{ROOT_DIR}models/01-ai/Yi-34B-Chat", meta_template=yi_meta_template, num_gpus=4) | ||
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| yi_bases = [yi_6b, yi_9b, yi_34b] | ||
| yi_chats = [yi_6b_chat, yi_9b_chat, yi_34b_chat] | ||
| yi_all = yi_bases + yi_chats | ||
| yi_chats = [yi_6b_chat, | ||
| # yi_9b_chat, | ||
| yi_34b_chat] | ||
| yi_all = yi_bases + yi_chats | ||
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| yi_6b_vllm = get_vllm_model(abbr="yi-6b", path=f"{ROOT_DIR}models/01-ai/Yi-6B") | ||
| yi_9b_vllm = get_vllm_model(abbr="yi-9b", path=f"{ROOT_DIR}models/01-ai/Yi-9B") | ||
| yi_34b_vllm = get_vllm_model(abbr="yi-34b", path=f"{ROOT_DIR}models/01-ai/Yi-34B", num_gpus=2) | ||
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| yi_6b_chat_vllm = get_vllm_model(abbr="yi-6b-chat", path=f"{ROOT_DIR}models/01-ai/Yi-6B-Chat", meta_template=yi_meta_template) | ||
| # yi_9b_chat_vllm = get_vllm_model(abbr="yi-9b-chat", path=f"{ROOT_DIR}models/01-ai/Yi-9B-Chat", meta_template=yi_meta_template) | ||
| yi_34b_chat_vllm = get_vllm_model(abbr="yi-34b-chat", path=f"{ROOT_DIR}models/01-ai/Yi-34B-Chat", meta_template=yi_meta_template, num_gpus=4) | ||
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| yi_bases_vllm = [yi_6b_vllm, yi_9b_vllm, yi_34b_vllm] | ||
| yi_chats_vllm = [yi_6b_chat_vllm, | ||
| # yi_9b_chat_vllm, | ||
| yi_34b_chat_vllm] | ||
| yi_all_vllm = yi_bases_vllm + yi_chats_vllm |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,69 @@ | ||
| from mmengine.config import read_base | ||
| from opencompass.partitioners import SizePartitioner, NaivePartitioner | ||
| from opencompass.runners import LocalRunner | ||
| from opencompass.tasks import OpenICLInferTask, OpenICLEvalTask | ||
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| with read_base(): | ||
| # Datasets | ||
| from ..datasets.opseval.datasets import all_gen_qa | ||
| # Models | ||
| from ..local_models.baichuan.baichuan import baichuan2_chats_vllm | ||
| from ..local_models.google.gemma import gemma_vllm | ||
| from ..local_models.internlm.internlm import internlm2_chats_vllm | ||
| from ..local_models.lmsys.vicuna import vicuna_bases_vllm | ||
| from ..local_models.mistral.mistral import mistral_7b_vllm | ||
| from ..local_models.qwen.qwen import qwen1_5_chat_vllm_models | ||
| from ..local_models.yi.yi import yi_all_vllm | ||
| from ..models.gpt_3dot5_turbo_peiqi import models as peiqi_models | ||
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| from ..paths import ROOT_DIR | ||
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| datasets = [ | ||
| *all_gen_qa | ||
| ] | ||
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| # datasets = [datasets[0]] | ||
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| models = [ | ||
| *peiqi_models, | ||
| *baichuan2_chats_vllm, | ||
| *gemma_vllm, | ||
| *internlm2_chats_vllm, | ||
| *vicuna_bases_vllm, | ||
| *yi_all_vllm, | ||
| mistral_7b_vllm, | ||
| *qwen1_5_chat_vllm_models, | ||
| ] | ||
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| for dataset in datasets: | ||
| dataset['sample_setting'] = dict() | ||
| dataset['infer_cfg']['inferencer']['save_every'] = 8 | ||
| dataset['infer_cfg']['inferencer']['sc_size'] = 1 | ||
| dataset['infer_cfg']['inferencer']['max_token_len'] = 200 | ||
| dataset['eval_cfg']['sc_size'] = 1 | ||
| # dataset['sample_setting'] = dict(sample_size=2) # !!!WARNING: Use for testing only!!! | ||
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| infer = dict( | ||
| partitioner=dict( | ||
| # type=SizePartitioner, | ||
| # max_task_size=100, | ||
| # gen_task_coef=1, | ||
| type=NaivePartitioner | ||
| ), | ||
| runner=dict( | ||
| type=LocalRunner, | ||
| max_num_workers=16, | ||
| max_workers_per_gpu=1, | ||
| task=dict(type=OpenICLInferTask), | ||
| ), | ||
| ) | ||
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| eval = dict( | ||
| partitioner=dict(type=NaivePartitioner), | ||
| runner=dict( | ||
| type=LocalRunner, | ||
| max_num_workers=32, | ||
| task=dict(type=OpenICLEvalTask)), | ||
| ) |
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