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OpenDAS
vllm_cscc
Commits
b6247705
Commit
b6247705
authored
Jul 06, 2024
by
zhuwenwen
Browse files
fix ray error when tp>=2
parent
045b5ad2
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examples/offline_inference.py
examples/offline_inference.py
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examples/offline_inference.py
View file @
b6247705
from
vllm
import
LLM
,
SamplingParams
from
vllm
import
LLM
,
SamplingParams
# Sample prompts.
if
__name__
==
'__main__'
:
prompts
=
[
# Sample prompts.
prompts
=
[
"Hello, my name is"
,
"Hello, my name is"
,
"The president of the United States is"
,
"The president of the United States is"
,
"The capital of France is"
,
"The capital of France is"
,
"The future of AI is"
,
"The future of AI is"
,
]
]
# Create a sampling params object.
# Create a sampling params object.
sampling_params
=
SamplingParams
(
temperature
=
0.8
,
top_p
=
0.95
)
sampling_params
=
SamplingParams
(
temperature
=
0.8
,
top_p
=
0.95
)
# Create an LLM.
# Create an LLM.
llm
=
LLM
(
model
=
"facebook/opt-125m"
,
trust_remote_code
=
True
,
dtype
=
"float16"
,
enforce_eager
=
True
)
llm
=
LLM
(
model
=
"facebook/opt-125m"
,
trust_remote_code
=
True
,
dtype
=
"float16"
,
enforce_eager
=
True
)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
# that contain the prompt, generated text, and other information.
outputs
=
llm
.
generate
(
prompts
,
sampling_params
)
outputs
=
llm
.
generate
(
prompts
,
sampling_params
)
# Print the outputs.
# Print the outputs.
for
output
in
outputs
:
for
output
in
outputs
:
prompt
=
output
.
prompt
prompt
=
output
.
prompt
generated_text
=
output
.
outputs
[
0
].
text
generated_text
=
output
.
outputs
[
0
].
text
print
(
f
"Prompt:
{
prompt
!
r
}
, Generated text:
{
generated_text
!
r
}
"
)
print
(
f
"Prompt:
{
prompt
!
r
}
, Generated text:
{
generated_text
!
r
}
"
)
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