Training requires upgrading transformers from 4.49.0 due to dtype mismatch error, causing potential performance concerns #9
Description
Excellent work!
However, I'm encountering an environment-related issue. I would greatly appreciate your help in resolving it.
I set up the environment according to your configuration in setup.sh:
Package Version Editable project location
--------------------------------- ------------- -----------------------------------------------
accelerate 1.4.0
aiohappyeyeballs 2.6.1
aiohttp 3.11.18
aiohttp-cors 0.8.1
aiosignal 1.3.2
airportsdata 20250224
annotated-types 0.7.0
antlr4-python3-runtime 4.13.2
anyio 4.9.0
astor 0.8.1
async-timeout 5.0.1
attrs 25.3.0
av 14.3.0
bitsandbytes 0.45.5
black 25.1.0
blake3 1.0.4
cachetools 5.5.2
certifi 2025.4.26
charset-normalizer 3.4.2
click 8.1.8
cloudpickle 3.1.1
colorful 0.5.6
compressed-tensors 0.9.1
datasets 3.6.0
deepspeed 0.15.4
depyf 0.18.0
dill 0.3.8
diskcache 5.6.3
distlib 0.3.9
distro 1.9.0
docker-pycreds 0.4.0
einops 0.8.1
exceptiongroup 1.2.2
fastapi 0.115.12
filelock 3.18.0
flake8 7.2.0
flash_attn 2.7.4.post1
frozenlist 1.6.0
fsspec 2025.3.0
gguf 0.10.0
gitdb 4.0.12
GitPython 3.1.44
google-api-core 2.24.2
google-auth 2.40.1
googleapis-common-protos 1.70.0
grpcio 1.71.0
h11 0.16.0
hf_transfer 0.1.9
hf-xet 1.1.0
hjson 3.1.0
httpcore 1.0.9
httptools 0.6.4
httpx 0.28.1
huggingface-hub 0.31.1
idna 3.10
importlib_metadata 8.7.0
iniconfig 2.1.0
inquirerpy 0.3.4
interegular 0.3.3
isort 6.0.1
Jinja2 3.1.6
jiter 0.9.0
jsonschema 4.23.0
jsonschema-specifications 2025.4.1
lark 1.2.2
latex2sympy2_extended 1.10.1
liger_kernel 0.5.2
lm-format-enforcer 0.10.11
markdown-it-py 3.0.0
MarkupSafe 3.0.2
math-verify 0.7.0
mccabe 0.7.0
mdurl 0.1.2
mistral_common 1.5.4
mpmath 1.3.0
msgpack 1.1.0
msgspec 0.19.0
multidict 6.4.3
multiprocess 0.70.16
mypy_extensions 1.1.0
nest-asyncio 1.6.0
networkx 3.4.2
ninja 1.11.1.4
numpy 1.26.4
nvidia-cublas-cu12 12.4.5.8
nvidia-cuda-cupti-cu12 12.4.127
nvidia-cuda-nvrtc-cu12 12.4.127
nvidia-cuda-runtime-cu12 12.4.127
nvidia-cudnn-cu12 9.1.0.70
nvidia-cufft-cu12 11.2.1.3
nvidia-cufile-cu12 1.11.1.6
nvidia-curand-cu12 10.3.5.147
nvidia-cusolver-cu12 11.6.1.9
nvidia-cusparse-cu12 12.3.1.170
nvidia-cusparselt-cu12 0.6.3
nvidia-ml-py 12.575.51
nvidia-nccl-cu12 2.21.5
nvidia-nvjitlink-cu12 12.4.127
nvidia-nvtx-cu12 12.4.127
open-r1 0.1.0.dev0 /data1_hdd/yaxiong/zyc_temp/UI-R1/src/ui_r1/src
openai 1.77.0
opencensus 0.11.4
opencensus-context 0.1.3
opencv-python-headless 4.11.0.86
outlines 0.1.11
outlines_core 0.1.26
packaging 25.0
pandas 2.2.3
parameterized 0.9.0
partial-json-parser 0.2.1.1.post5
pathspec 0.12.1
pfzy 0.3.4
pillow 11.2.1
pip 25.1
platformdirs 4.3.8
pluggy 1.5.0
prometheus_client 0.21.1
prometheus-fastapi-instrumentator 7.1.0
prompt_toolkit 3.0.51
propcache 0.3.1
proto-plus 1.26.1
protobuf 5.29.4
psutil 7.0.0
py-cpuinfo 9.0.0
py-spy 0.4.0
pyarrow 20.0.0
pyasn1 0.6.1
pyasn1_modules 0.4.2
pycodestyle 2.13.0
pycountry 24.6.1
pydantic 2.11.4
pydantic_core 2.33.2
pyflakes 3.3.2
Pygments 2.19.1
pytest 8.3.5
python-dateutil 2.9.0.post0
python-dotenv 1.1.0
pytz 2025.2
PyYAML 6.0.2
pyzmq 26.4.0
qwen-vl-utils 0.0.11
ray 2.46.0
referencing 0.36.2
regex 2024.11.6
requests 2.32.3
rich 14.0.0
rpds-py 0.24.0
rsa 4.9.1
safetensors 0.5.3
sentencepiece 0.2.0
sentry-sdk 2.27.0
setproctitle 1.3.6
setuptools 78.1.1
six 1.17.0
smart-open 7.1.0
smmap 5.0.2
sniffio 1.3.1
starlette 0.46.2
sympy 1.13.1
tensorboardX 2.6.2.2
tiktoken 0.9.0
tokenizers 0.21.1
tomli 2.2.1
torch 2.5.1
torchaudio 2.5.1
torchvision 0.20.1
tqdm 4.67.1
transformers 4.49.0
triton 3.1.0
trl 0.16.0
typing_extensions 4.13.2
typing-inspection 0.4.0
tzdata 2025.2
urllib3 2.4.0
uvicorn 0.34.2
uvloop 0.21.0
virtualenv 20.31.1
vllm 0.7.2
wandb 0.18.3
watchfiles 1.0.5
wcwidth 0.2.13
websockets 15.0.1
wheel 0.45.1
wrapt 1.17.2
xformers 0.0.28.post3
xgrammar 0.1.19
xxhash 3.5.0
yarl 1.20.0
zipp 3.21.0
Run train.sh with this environment will report errors：
[rank1]: AssertionError: Input and cos/sin must have the same dtype, got torch.float32 and torch.bfloat16
I noticed that is a bug from transformers=4.49.0(modelscope/ms-swift#3156)
So I upgraded transformers to 4.50.3. At this time, train.sh can run normally, but there will be an environment ERR
open-r1 0.1.0.dev0 requires transformers==4.49.0, but you have transformers 4.50.3 which is incompatible.
So, will using transformers=4.50.3 this way have an impact on model performance? Because I can train normally using this configuration, but after training 8 epochs, the accuracy of the model is only about 20%.