OpenCompass/configs/models/hf_llama/vllm_llama_series.py
Linchen Xiao 8e55c9c6ee
[Update] Compassbench v1.3 (#1396)
* stash files

* compassbench subjective evaluation added

* evaluation update

* fix lint

* update docs

* Update lint

* changes saved

* changes saved

* CompassBench subjective summarizer added (#1349)

* subjective summarizer added

* fix lint

[Fix] Fix MathBench (#1351)

Co-authored-by: liuhongwei <liuhongwei@pjlab.org.cn>

[Update] Update model support list (#1353)

* fix pip version

* fix pip version

* update model support

subjective summarizer updated

knowledge, math objective done (data need update)

remove secrets

objective changes saved

knowledge data added

* secrets removed

* changed added

* summarizer modified

* summarizer modified

* compassbench coding added

* fix lint

* objective summarizer updated

* compass_bench_v1.3 updated

* update files in config folder

* remove unused model

* lcbench modified

* removed model evaluation configs

* remove duplicated sdk implementation

---------

Co-authored-by: zhangsongyang <zhangsongyang@pjlab.org.cn>
2024-08-12 19:09:19 +08:00

33 lines
1.2 KiB
Python

from opencompass.models import VLLM
settings = [
('llama-7b-vllm', 'huggyllama/llama-7b', 1),
('llama-13b-vllm', 'huggyllama/llama-13b', 1),
('llama-30b-vllm', 'huggyllama/llama-30b', 2),
('llama-65b-vllm', 'huggyllama/llama-65b', 4),
('llama-2-7b-vllm', 'meta-llama/Llama-2-7b-hf', 1),
('llama-2-13b-vllm', 'meta-llama/Llama-2-13b-hf', 1),
('llama-2-70b-vllm', 'meta-llama/Llama-2-70b-hf', 4),
('llama-3-8b-vllm', 'meta-llama/Meta-Llama-3-8B', 1),
('llama-3-70b-vllm', 'meta-llama/Meta-Llama-3-70B', 4),
('llama-3.1-8b-instruct-vllm', 'meta-llama/Meta-Llama-3.1-8B-Instruct', 1)
('llama-3.1-70b-instruct-vllm', 'meta-llama/Meta-Llama-3.1-70B-Instruct', 4)
('llama-3.1-405b-fp8-instruct-vllm', 'meta-llama/Meta-Llama-3.1-405B-Instruct-FP8', 8)
]
models = []
for abbr, path, num_gpus in settings:
models.append(
dict(
type=VLLM,
abbr=abbr,
path=path,
model_kwargs=dict(tensor_parallel_size=num_gpus),
max_out_len=100,
max_seq_len=2048,
batch_size=32,
generation_kwargs=dict(temperature=0),
run_cfg=dict(num_gpus=num_gpus, num_procs=1),
)
)