ClearML Task: overwriting (reusing) task id=c1393538e4394832aeb1fbefd1138499
2025-05-15 22:51:31,007 - clearml.Task - INFO - No repository found, storing script code instead
/home/alexandre/spft/axolotl/venv/lib/python3.10/site-packages/google/auth/_default.py:76: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a "quota exceeded" or "API not enabled" error. See the following page for troubleshooting: https://cloud.google.com/docs/authentication/adc-troubleshooting/user-creds. 
  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)
[2025-05-15 22:51:38,558] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-15 22:51:38,625] [INFO] [root.spawn:77] [PID:2204328] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpyoys4b8u/test.c -o /tmp/tmpyoys4b8u/test.o
[2025-05-15 22:51:38,641] [INFO] [root.spawn:77] [PID:2204328] x86_64-linux-gnu-gcc /tmp/tmpyoys4b8u/test.o -laio -o /tmp/tmpyoys4b8u/a.out
[2025-05-15 22:51:38,657] [INFO] [root.spawn:77] [PID:2204328] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpy50ah3qc/test.c -o /tmp/tmpy50ah3qc/test.o
[2025-05-15 22:51:38,672] [INFO] [root.spawn:77] [PID:2204328] x86_64-linux-gnu-gcc /tmp/tmpy50ah3qc/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmpy50ah3qc/a.out
[2025-05-15 22:51:38,708] [INFO] [root.spawn:77] [PID:2204328] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpyicz15p2/test.c -o /tmp/tmpyicz15p2/test.o
[2025-05-15 22:51:38,722] [INFO] [root.spawn:77] [PID:2204328] x86_64-linux-gnu-gcc /tmp/tmpyicz15p2/test.o -laio -o /tmp/tmpyicz15p2/a.out
WARNING:accelerate.commands.launch:The following values were not passed to `accelerate launch` and had defaults used instead:
	`--num_processes` was set to a value of `8`
		More than one GPU was found, enabling multi-GPU training.
		If this was unintended please pass in `--num_processes=1`.
	`--num_machines` was set to a value of `1`
	`--mixed_precision` was set to a value of `'no'`
	`--dynamo_backend` was set to a value of `'no'`
To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
[2025-05-15 22:51:50,065] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-15 22:51:50,096] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-15 22:51:50,125] [INFO] [root.spawn:77] [PID:2204919] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmp73lpcrwx/test.c -o /tmp/tmp73lpcrwx/test.o
[2025-05-15 22:51:50,125] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-15 22:51:50,140] [INFO] [root.spawn:77] [PID:2204919] x86_64-linux-gnu-gcc /tmp/tmp73lpcrwx/test.o -laio -o /tmp/tmp73lpcrwx/a.out
[2025-05-15 22:51:50,143] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-15 22:51:50,151] [INFO] [root.spawn:77] [PID:2204919] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmp5yxnhm2u/test.c -o /tmp/tmp5yxnhm2u/test.o
[2025-05-15 22:51:50,157] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-15 22:51:50,159] [INFO] [root.spawn:77] [PID:2204920] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpsdmi593h/test.c -o /tmp/tmpsdmi593h/test.o
[2025-05-15 22:51:50,166] [INFO] [root.spawn:77] [PID:2204919] x86_64-linux-gnu-gcc /tmp/tmp5yxnhm2u/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmp5yxnhm2u/a.out
[2025-05-15 22:51:50,176] [INFO] [root.spawn:77] [PID:2204920] x86_64-linux-gnu-gcc /tmp/tmpsdmi593h/test.o -laio -o /tmp/tmpsdmi593h/a.out
[2025-05-15 22:51:50,184] [INFO] [root.spawn:77] [PID:2204915] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmphcv_cs1x/test.c -o /tmp/tmphcv_cs1x/test.o
[2025-05-15 22:51:50,190] [INFO] [root.spawn:77] [PID:2204920] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpp6f5v3z3/test.c -o /tmp/tmpp6f5v3z3/test.o
[2025-05-15 22:51:50,199] [INFO] [root.spawn:77] [PID:2204915] x86_64-linux-gnu-gcc /tmp/tmphcv_cs1x/test.o -laio -o /tmp/tmphcv_cs1x/a.out
[2025-05-15 22:51:50,200] [INFO] [root.spawn:77] [PID:2204919] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmp_xn797lf/test.c -o /tmp/tmp_xn797lf/test.o
[2025-05-15 22:51:50,203] [INFO] [root.spawn:77] [PID:2204917] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpxd7k_vil/test.c -o /tmp/tmpxd7k_vil/test.o
[2025-05-15 22:51:50,205] [INFO] [root.spawn:77] [PID:2204920] x86_64-linux-gnu-gcc /tmp/tmpp6f5v3z3/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmpp6f5v3z3/a.out
[2025-05-15 22:51:50,206] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-15 22:51:50,210] [INFO] [root.spawn:77] [PID:2204915] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpmxr0y1zz/test.c -o /tmp/tmpmxr0y1zz/test.o
[2025-05-15 22:51:50,214] [INFO] [root.spawn:77] [PID:2204919] x86_64-linux-gnu-gcc /tmp/tmp_xn797lf/test.o -laio -o /tmp/tmp_xn797lf/a.out
[2025-05-15 22:51:50,216] [INFO] [root.spawn:77] [PID:2204916] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpwntzrrzu/test.c -o /tmp/tmpwntzrrzu/test.o
[2025-05-15 22:51:50,218] [INFO] [root.spawn:77] [PID:2204917] x86_64-linux-gnu-gcc /tmp/tmpxd7k_vil/test.o -laio -o /tmp/tmpxd7k_vil/a.out
[2025-05-15 22:51:50,223] [INFO] [root.spawn:77] [PID:2204915] x86_64-linux-gnu-gcc /tmp/tmpmxr0y1zz/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmpmxr0y1zz/a.out
[2025-05-15 22:51:50,229] [INFO] [root.spawn:77] [PID:2204917] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmp2sx66j8j/test.c -o /tmp/tmp2sx66j8j/test.o
[2025-05-15 22:51:50,231] [INFO] [root.spawn:77] [PID:2204916] x86_64-linux-gnu-gcc /tmp/tmpwntzrrzu/test.o -laio -o /tmp/tmpwntzrrzu/a.out
[2025-05-15 22:51:50,240] [INFO] [root.spawn:77] [PID:2204920] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmp0lj7aic7/test.c -o /tmp/tmp0lj7aic7/test.o
[2025-05-15 22:51:50,242] [INFO] [root.spawn:77] [PID:2204916] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmp12nb7ydo/test.c -o /tmp/tmp12nb7ydo/test.o
[2025-05-15 22:51:50,246] [INFO] [root.spawn:77] [PID:2204917] x86_64-linux-gnu-gcc /tmp/tmp2sx66j8j/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmp2sx66j8j/a.out
[2025-05-15 22:51:50,255] [INFO] [root.spawn:77] [PID:2204916] x86_64-linux-gnu-gcc /tmp/tmp12nb7ydo/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmp12nb7ydo/a.out
[2025-05-15 22:51:50,257] [INFO] [root.spawn:77] [PID:2204915] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpwgk7ni43/test.c -o /tmp/tmpwgk7ni43/test.o
[2025-05-15 22:51:50,258] [INFO] [root.spawn:77] [PID:2204920] x86_64-linux-gnu-gcc /tmp/tmp0lj7aic7/test.o -laio -o /tmp/tmp0lj7aic7/a.out
[2025-05-15 22:51:50,267] [INFO] [root.spawn:77] [PID:2204918] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmp799bphcy/test.c -o /tmp/tmp799bphcy/test.o
[2025-05-15 22:51:50,271] [INFO] [root.spawn:77] [PID:2204915] x86_64-linux-gnu-gcc /tmp/tmpwgk7ni43/test.o -laio -o /tmp/tmpwgk7ni43/a.out
[2025-05-15 22:51:50,281] [INFO] [root.spawn:77] [PID:2204917] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpqvf_g8ly/test.c -o /tmp/tmpqvf_g8ly/test.o
[2025-05-15 22:51:50,283] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-15 22:51:50,286] [INFO] [root.spawn:77] [PID:2204918] x86_64-linux-gnu-gcc /tmp/tmp799bphcy/test.o -laio -o /tmp/tmp799bphcy/a.out
[2025-05-15 22:51:50,290] [INFO] [root.spawn:77] [PID:2204916] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpft_h_ct7/test.c -o /tmp/tmpft_h_ct7/test.o
[2025-05-15 22:51:50,294] [INFO] [root.spawn:77] [PID:2204917] x86_64-linux-gnu-gcc /tmp/tmpqvf_g8ly/test.o -laio -o /tmp/tmpqvf_g8ly/a.out
[2025-05-15 22:51:50,300] [INFO] [root.spawn:77] [PID:2204918] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmplgh8faiu/test.c -o /tmp/tmplgh8faiu/test.o
[2025-05-15 22:51:50,304] [INFO] [root.spawn:77] [PID:2204916] x86_64-linux-gnu-gcc /tmp/tmpft_h_ct7/test.o -laio -o /tmp/tmpft_h_ct7/a.out
[2025-05-15 22:51:50,315] [INFO] [root.spawn:77] [PID:2204918] x86_64-linux-gnu-gcc /tmp/tmplgh8faiu/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmplgh8faiu/a.out
[2025-05-15 22:51:50,330] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-15 22:51:50,344] [INFO] [root.spawn:77] [PID:2204921] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpl_q76i03/test.c -o /tmp/tmpl_q76i03/test.o
[2025-05-15 22:51:50,350] [INFO] [root.spawn:77] [PID:2204918] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpd863qkgz/test.c -o /tmp/tmpd863qkgz/test.o
[2025-05-15 22:51:50,358] [INFO] [root.spawn:77] [PID:2204921] x86_64-linux-gnu-gcc /tmp/tmpl_q76i03/test.o -laio -o /tmp/tmpl_q76i03/a.out
[2025-05-15 22:51:50,364] [INFO] [root.spawn:77] [PID:2204918] x86_64-linux-gnu-gcc /tmp/tmpd863qkgz/test.o -laio -o /tmp/tmpd863qkgz/a.out
[2025-05-15 22:51:50,373] [INFO] [root.spawn:77] [PID:2204921] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmp011kwcbo/test.c -o /tmp/tmp011kwcbo/test.o
[2025-05-15 22:51:50,389] [INFO] [root.spawn:77] [PID:2204921] x86_64-linux-gnu-gcc /tmp/tmp011kwcbo/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmp011kwcbo/a.out
[2025-05-15 22:51:50,393] [INFO] [root.spawn:77] [PID:2204922] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpju0cfv10/test.c -o /tmp/tmpju0cfv10/test.o
[2025-05-15 22:51:50,408] [INFO] [root.spawn:77] [PID:2204922] x86_64-linux-gnu-gcc /tmp/tmpju0cfv10/test.o -laio -o /tmp/tmpju0cfv10/a.out
[2025-05-15 22:51:50,420] [INFO] [root.spawn:77] [PID:2204922] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpebubdx4f/test.c -o /tmp/tmpebubdx4f/test.o
[2025-05-15 22:51:50,425] [INFO] [root.spawn:77] [PID:2204921] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmplkjb4xt5/test.c -o /tmp/tmplkjb4xt5/test.o
[2025-05-15 22:51:50,435] [INFO] [root.spawn:77] [PID:2204922] x86_64-linux-gnu-gcc /tmp/tmpebubdx4f/test.o -L/usr/local/cuda -L/usr/local/cuda/lib64 -lcufile -o /tmp/tmpebubdx4f/a.out
[2025-05-15 22:51:50,440] [INFO] [root.spawn:77] [PID:2204921] x86_64-linux-gnu-gcc /tmp/tmplkjb4xt5/test.o -laio -o /tmp/tmplkjb4xt5/a.out
[2025-05-15 22:51:50,470] [INFO] [root.spawn:77] [PID:2204922] x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -c /tmp/tmpr_6_rwf8/test.c -o /tmp/tmpr_6_rwf8/test.o
[2025-05-15 22:51:50,484] [INFO] [root.spawn:77] [PID:2204922] x86_64-linux-gnu-gcc /tmp/tmpr_6_rwf8/test.o -laio -o /tmp/tmpr_6_rwf8/a.out
[2025-05-15 22:51:50,650] [INFO] [datasets.<module>:54] [PID:2204919] PyTorch version 2.7.0 available.
[2025-05-15 22:51:50,673] [INFO] [datasets.<module>:54] [PID:2204920] PyTorch version 2.7.0 available.
[2025-05-15 22:51:50,696] [INFO] [datasets.<module>:54] [PID:2204917] PyTorch version 2.7.0 available.
[2025-05-15 22:51:50,697] [INFO] [datasets.<module>:54] [PID:2204915] PyTorch version 2.7.0 available.
[2025-05-15 22:51:50,714] [INFO] [datasets.<module>:54] [PID:2204916] PyTorch version 2.7.0 available.
[2025-05-15 22:51:50,776] [INFO] [datasets.<module>:54] [PID:2204918] PyTorch version 2.7.0 available.
[2025-05-15 22:51:50,880] [INFO] [datasets.<module>:54] [PID:2204921] PyTorch version 2.7.0 available.
[2025-05-15 22:51:50,890] [INFO] [datasets.<module>:54] [PID:2204922] PyTorch version 2.7.0 available.
[2025-05-15 22:51:52,153] [INFO] [root.register:348] [PID:2204915] Attempting to load plugin: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,154] [INFO] [root.register:351] [PID:2204915] Plugin loaded successfully: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,154] [INFO] [root.register:348] [PID:2204915] Attempting to load plugin: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,184] [INFO] [root.register:348] [PID:2204919] Attempting to load plugin: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,186] [INFO] [root.register:351] [PID:2204919] Plugin loaded successfully: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,186] [INFO] [root.register:348] [PID:2204919] Attempting to load plugin: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,188] [INFO] [root.register:348] [PID:2204920] Attempting to load plugin: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,191] [INFO] [root.register:351] [PID:2204920] Plugin loaded successfully: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,191] [INFO] [root.register:348] [PID:2204920] Attempting to load plugin: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,227] [INFO] [root.register:348] [PID:2204917] Attempting to load plugin: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,229] [INFO] [root.register:351] [PID:2204917] Plugin loaded successfully: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,229] [INFO] [root.register:348] [PID:2204917] Attempting to load plugin: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,237] [INFO] [root.register:351] [PID:2204915] Plugin loaded successfully: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,277] [INFO] [root.register:351] [PID:2204919] Plugin loaded successfully: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,282] [INFO] [root.register:351] [PID:2204920] Plugin loaded successfully: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,296] [INFO] [root.register:348] [PID:2204916] Attempting to load plugin: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,298] [INFO] [root.register:351] [PID:2204916] Plugin loaded successfully: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,298] [INFO] [root.register:348] [PID:2204916] Attempting to load plugin: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,318] [INFO] [root.register:351] [PID:2204917] Plugin loaded successfully: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,348] [INFO] [root.register:348] [PID:2204922] Attempting to load plugin: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,349] [INFO] [root.register:351] [PID:2204922] Plugin loaded successfully: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,349] [INFO] [root.register:348] [PID:2204922] Attempting to load plugin: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,353] [INFO] [root.register:348] [PID:2204918] Attempting to load plugin: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,355] [INFO] [root.register:351] [PID:2204918] Plugin loaded successfully: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,355] [INFO] [root.register:348] [PID:2204918] Attempting to load plugin: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,390] [INFO] [root.register:351] [PID:2204916] Plugin loaded successfully: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,433] [INFO] [root.register:351] [PID:2204922] Plugin loaded successfully: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,441] [INFO] [root.register:351] [PID:2204918] Plugin loaded successfully: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,443] [INFO] [root.register:348] [PID:2204921] Attempting to load plugin: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,445] [INFO] [root.register:351] [PID:2204921] Plugin loaded successfully: axolotl.integrations.liger.LigerPlugin
[2025-05-15 22:51:52,445] [INFO] [root.register:348] [PID:2204921] Attempting to load plugin: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:52,534] [INFO] [root.register:351] [PID:2204921] Plugin loaded successfully: axolotl.integrations.llm_compressor.LLMCompressorPlugin
[2025-05-15 22:51:54,507] [DEBUG] [axolotl.resolve_dtype:65] [PID:2204917] [RANK:2] bf16 support detected, enabling for this configuration.
[2025-05-15 22:51:54,514] [DEBUG] [axolotl.resolve_dtype:65] [PID:2204919] [RANK:4] bf16 support detected, enabling for this configuration.
[2025-05-15 22:51:54,516] [DEBUG] [axolotl.resolve_dtype:65] [PID:2204921] [RANK:6] bf16 support detected, enabling for this configuration.
[2025-05-15 22:51:54,518] [DEBUG] [axolotl.resolve_dtype:65] [PID:2204920] [RANK:5] bf16 support detected, enabling for this configuration.
[2025-05-15 22:51:54,522] [DEBUG] [axolotl.resolve_dtype:65] [PID:2204915] [RANK:0] bf16 support detected, enabling for this configuration.
[2025-05-15 22:51:54,551] [DEBUG] [axolotl.resolve_dtype:65] [PID:2204918] [RANK:3] bf16 support detected, enabling for this configuration.
[2025-05-15 22:51:54,557] [INFO] [axolotl.normalize_config:237] [PID:2204920] [RANK:5] cuda memory usage baseline: 0.000GB (+0.461GB misc)
[2025-05-15 22:51:54,565] [INFO] [axolotl.normalize_config:237] [PID:2204919] [RANK:4] cuda memory usage baseline: 0.000GB (+0.461GB misc)
[2025-05-15 22:51:54,565] [INFO] [axolotl.normalize_config:237] [PID:2204921] [RANK:6] cuda memory usage baseline: 0.000GB (+0.461GB misc)
[2025-05-15 22:51:54,571] [INFO] [axolotl.normalize_config:237] [PID:2204915] [RANK:0] cuda memory usage baseline: 0.000GB (+0.461GB misc)
[2025-05-15 22:51:54,574] [DEBUG] [axolotl.resolve_dtype:65] [PID:2204916] [RANK:1] bf16 support detected, enabling for this configuration.
[2025-05-15 22:51:54,606] [INFO] [axolotl.normalize_config:237] [PID:2204918] [RANK:3] cuda memory usage baseline: 0.000GB (+0.461GB misc)
[2025-05-15 22:51:54,608] [INFO] [axolotl.normalize_config:237] [PID:2204917] [RANK:2] cuda memory usage baseline: 0.000GB (+0.461GB misc)
[2025-05-15 22:51:54,612] [DEBUG] [axolotl.resolve_dtype:65] [PID:2204922] [RANK:7] bf16 support detected, enabling for this configuration.
[2025-05-15 22:51:54,630] [INFO] [axolotl.normalize_config:237] [PID:2204916] [RANK:1] cuda memory usage baseline: 0.000GB (+0.461GB misc)
[2025-05-15 22:51:54,680] [INFO] [axolotl.normalize_config:237] [PID:2204922] [RANK:7] cuda memory usage baseline: 0.000GB (+0.461GB misc)

     #@@ #@@      @@# @@#
    @@  @@          @@  @@           =@@#                               @@                 #@    =@@#.
    @@    #@@@@@@@@@    @@           #@#@=                              @@                 #@     .=@@
      #@@@@@@@@@@@@@@@@@            =@# @#     ##=     ##    =####=+    @@      =#####+  =#@@###.   @@
    @@@@@@@@@@/  +@@/  +@@          #@  =@=     #@=   @@   =@#+  +#@#   @@    =@#+  +#@#   #@.      @@
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     @@@@@@@@@@@@@@@@@@@@          #@=+++#@=      =@@#     @@      @@   @@    @@      #@   #@       @@
                                  =@#=====@@     =@# @#    @@      @@   @@    @@      #@   #@       @@
    @@@@@@@@@@@@@@@@  @@@@        #@      #@=   #@=  +@@   #@#    =@#   @@.   =@#    =@#   #@.      @@
                                 =@#       @#  #@=     #@   =#@@@@#=    +#@@=  +#@@@@#=    .##@@+   @@
    @@@@  @@@@@@@@@@@@@@@@

[2025-05-15 22:51:56,573] [DEBUG] [axolotl.utils.models.load_tokenizer:461] [PID:2204915] [RANK:0] EOS: 128001 / <|end_of_text|>
[2025-05-15 22:51:56,574] [DEBUG] [axolotl.utils.models.load_tokenizer:462] [PID:2204915] [RANK:0] BOS: 128000 / <|begin_of_text|>
[2025-05-15 22:51:56,574] [DEBUG] [axolotl.utils.models.load_tokenizer:463] [PID:2204915] [RANK:0] PAD: 128001 / <|end_of_text|>
[2025-05-15 22:51:56,574] [DEBUG] [axolotl.utils.models.load_tokenizer:464] [PID:2204915] [RANK:0] UNK: None / None
[2025-05-15 22:51:56,574] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204915] [RANK:0] No Chat template selected. Consider adding a chat template for easier inference.
[2025-05-15 22:51:56,574] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:260] [PID:2204915] [RANK:0] Loading prepared dataset from disk at last_run_prepared/34fd097a6fdc6f75f0090b4a1d5dd3a0...
[2025-05-15 22:51:56,582] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:262] [PID:2204915] [RANK:0] Prepared dataset loaded from disk...
[rank0]:[W515 22:51:56.181082343 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
[2025-05-15 22:51:56,865] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204917] [RANK:2] No Chat template selected. Consider adding a chat template for easier inference.
[rank2]:[W515 22:51:56.245238440 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 2]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
[2025-05-15 22:51:56,872] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204918] [RANK:3] No Chat template selected. Consider adding a chat template for easier inference.
[2025-05-15 22:51:56,872] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204919] [RANK:4] No Chat template selected. Consider adding a chat template for easier inference.
[2025-05-15 22:51:56,879] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204916] [RANK:1] No Chat template selected. Consider adding a chat template for easier inference.
[rank4]:[W515 22:51:56.255395313 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 4]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
[rank3]:[W515 22:51:56.256440028 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 3]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
[rank1]:[W515 22:51:56.261842757 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 1]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
[2025-05-15 22:51:56,886] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204922] [RANK:7] No Chat template selected. Consider adding a chat template for easier inference.
[rank7]:[W515 22:51:56.271270856 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 7]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
[2025-05-15 22:51:56,903] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204920] [RANK:5] No Chat template selected. Consider adding a chat template for easier inference.
[rank5]:[W515 22:51:56.286513346 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 5]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
[2025-05-15 22:51:56,933] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204921] [RANK:6] No Chat template selected. Consider adding a chat template for easier inference.
[rank6]:[W515 22:51:56.313234716 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 6]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
[2025-05-15 22:51:59,229] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:260] [PID:2204919] [RANK:4] Loading prepared dataset from disk at last_run_prepared/34fd097a6fdc6f75f0090b4a1d5dd3a0...
[2025-05-15 22:51:59,229] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:260] [PID:2204918] [RANK:3] Loading prepared dataset from disk at last_run_prepared/34fd097a6fdc6f75f0090b4a1d5dd3a0...
[2025-05-15 22:51:59,229] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:260] [PID:2204922] [RANK:7] Loading prepared dataset from disk at last_run_prepared/34fd097a6fdc6f75f0090b4a1d5dd3a0...
[2025-05-15 22:51:59,229] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:260] [PID:2204921] [RANK:6] Loading prepared dataset from disk at last_run_prepared/34fd097a6fdc6f75f0090b4a1d5dd3a0...
[2025-05-15 22:51:59,230] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:260] [PID:2204916] [RANK:1] Loading prepared dataset from disk at last_run_prepared/34fd097a6fdc6f75f0090b4a1d5dd3a0...
[2025-05-15 22:51:59,230] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:260] [PID:2204917] [RANK:2] Loading prepared dataset from disk at last_run_prepared/34fd097a6fdc6f75f0090b4a1d5dd3a0...
[2025-05-15 22:51:59,230] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:260] [PID:2204920] [RANK:5] Loading prepared dataset from disk at last_run_prepared/34fd097a6fdc6f75f0090b4a1d5dd3a0...
[2025-05-15 22:51:59,242] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:262] [PID:2204919] [RANK:4] Prepared dataset loaded from disk...
[2025-05-15 22:51:59,242] [DEBUG] [axolotl.calculate_total_num_steps:405] [PID:2204915] [RANK:0] total_num_tokens: 2_253_747
[2025-05-15 22:51:59,245] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:262] [PID:2204922] [RANK:7] Prepared dataset loaded from disk...
[2025-05-15 22:51:59,246] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:262] [PID:2204921] [RANK:6] Prepared dataset loaded from disk...
[2025-05-15 22:51:59,247] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:262] [PID:2204918] [RANK:3] Prepared dataset loaded from disk...
[2025-05-15 22:51:59,247] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:262] [PID:2204917] [RANK:2] Prepared dataset loaded from disk...
[2025-05-15 22:51:59,247] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:262] [PID:2204920] [RANK:5] Prepared dataset loaded from disk...
[2025-05-15 22:51:59,250] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:262] [PID:2204916] [RANK:1] Prepared dataset loaded from disk...
[2025-05-15 22:51:59,304] [DEBUG] [axolotl.calculate_total_num_steps:423] [PID:2204915] [RANK:0] `total_supervised_tokens: 195_019`
[2025-05-15 22:52:00,630] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:434] [PID:2204915] [RANK:0] gather_len_batches: [559, 559, 559, 560, 559, 559, 559, 560]
[2025-05-15 22:52:00,631] [DEBUG] [axolotl.calculate_total_num_steps:481] [PID:2204915] [RANK:0] data_loader_len: 69
[2025-05-15 22:52:00,650] [INFO] [axolotl.calc_sample_packing_eff_est:491] [PID:2204915] [RANK:0] sample_packing_eff_est across ranks: [0.984313428401947, 0.984313428401947, 0.984313428401947, 0.9825556874275208, 0.984313428401947, 0.984313428401947, 0.984313428401947, 0.9825556874275208]
[2025-05-15 22:52:00,651] [DEBUG] [axolotl.calculate_total_num_steps:503] [PID:2204915] [RANK:0] sample_packing_eff_est: None
[2025-05-15 22:52:00,651] [DEBUG] [axolotl.calculate_total_num_steps:516] [PID:2204915] [RANK:0] total_num_steps: 69
[2025-05-15 22:52:00,810] [DEBUG] [axolotl.calculate_total_num_steps:405] [PID:2204915] [RANK:0] total_num_tokens: 42_816_897
[2025-05-15 22:52:01,591] [DEBUG] [axolotl.calculate_total_num_steps:423] [PID:2204915] [RANK:0] `total_supervised_tokens: 3_699_357`
[2025-05-15 22:52:02,200] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:434] [PID:2204915] [RANK:0] gather_len_batches: [10617, 10617, 10621, 10617, 10621, 10622, 10617, 10620]
[2025-05-15 22:52:02,201] [DEBUG] [axolotl.calculate_total_num_steps:481] [PID:2204915] [RANK:0] data_loader_len: 1327
[2025-05-15 22:52:02,202] [INFO] [axolotl.calc_sample_packing_eff_est:491] [PID:2204915] [RANK:0] sample_packing_eff_est across ranks: [0.9845854640007019, 0.9845854640007019, 0.9842146635055542, 0.9845854640007019, 0.9842146635055542, 0.9841220378875732, 0.9845854640007019, 0.9843073487281799]
[2025-05-15 22:52:02,202] [DEBUG] [axolotl.calculate_total_num_steps:503] [PID:2204915] [RANK:0] sample_packing_eff_est: 0.99
[2025-05-15 22:52:02,202] [DEBUG] [axolotl.calculate_total_num_steps:516] [PID:2204915] [RANK:0] total_num_steps: 1327
[2025-05-15 22:52:02,219] [DEBUG] [axolotl.train.setup_model_and_tokenizer:63] [PID:2204921] [RANK:6] loading tokenizer... RedHatAI/Sparse-Llama-3.1-8B-2of4
[2025-05-15 22:52:02,219] [DEBUG] [axolotl.train.setup_model_and_tokenizer:63] [PID:2204917] [RANK:2] loading tokenizer... RedHatAI/Sparse-Llama-3.1-8B-2of4
[2025-05-15 22:52:02,222] [DEBUG] [axolotl.train.setup_model_and_tokenizer:63] [PID:2204919] [RANK:4] loading tokenizer... RedHatAI/Sparse-Llama-3.1-8B-2of4
[2025-05-15 22:52:02,224] [DEBUG] [axolotl.train.setup_model_and_tokenizer:63] [PID:2204922] [RANK:7] loading tokenizer... RedHatAI/Sparse-Llama-3.1-8B-2of4
[2025-05-15 22:52:02,225] [DEBUG] [axolotl.train.setup_model_and_tokenizer:63] [PID:2204916] [RANK:1] loading tokenizer... RedHatAI/Sparse-Llama-3.1-8B-2of4
[2025-05-15 22:52:02,226] [DEBUG] [axolotl.train.setup_model_and_tokenizer:63] [PID:2204920] [RANK:5] loading tokenizer... RedHatAI/Sparse-Llama-3.1-8B-2of4
[2025-05-15 22:52:02,229] [DEBUG] [axolotl.train.setup_model_and_tokenizer:63] [PID:2204918] [RANK:3] loading tokenizer... RedHatAI/Sparse-Llama-3.1-8B-2of4
[2025-05-15 22:52:02,252] [DEBUG] [axolotl.train.setup_model_and_tokenizer:63] [PID:2204915] [RANK:0] loading tokenizer... RedHatAI/Sparse-Llama-3.1-8B-2of4
[2025-05-15 22:52:02,551] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204918] [RANK:3] No Chat template selected. Consider adding a chat template for easier inference.
[2025-05-15 22:52:02,552] [DEBUG] [axolotl.train.setup_model_and_tokenizer:77] [PID:2204918] [RANK:3] loading model
[2025-05-15 22:52:02,560] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204922] [RANK:7] No Chat template selected. Consider adding a chat template for easier inference.
[2025-05-15 22:52:02,560] [DEBUG] [axolotl.train.setup_model_and_tokenizer:77] [PID:2204922] [RANK:7] loading model
[2025-05-15 22:52:02,561] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204917] [RANK:2] No Chat template selected. Consider adding a chat template for easier inference.
[2025-05-15 22:52:02,561] [DEBUG] [axolotl.train.setup_model_and_tokenizer:77] [PID:2204917] [RANK:2] loading model
[2025-05-15 22:52:02,572] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204916] [RANK:1] No Chat template selected. Consider adding a chat template for easier inference.
[2025-05-15 22:52:02,572] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204920] [RANK:5] No Chat template selected. Consider adding a chat template for easier inference.
[2025-05-15 22:52:02,572] [DEBUG] [axolotl.train.setup_model_and_tokenizer:77] [PID:2204916] [RANK:1] loading model
[2025-05-15 22:52:02,572] [DEBUG] [axolotl.train.setup_model_and_tokenizer:77] [PID:2204920] [RANK:5] loading model
[2025-05-15 22:52:02,586] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204921] [RANK:6] No Chat template selected. Consider adding a chat template for easier inference.
[2025-05-15 22:52:02,586] [DEBUG] [axolotl.train.setup_model_and_tokenizer:77] [PID:2204921] [RANK:6] loading model
[2025-05-15 22:52:02,614] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204919] [RANK:4] No Chat template selected. Consider adding a chat template for easier inference.
[2025-05-15 22:52:02,614] [DEBUG] [axolotl.train.setup_model_and_tokenizer:77] [PID:2204919] [RANK:4] loading model
[2025-05-15 22:52:02,646] [DEBUG] [axolotl.utils.models.load_tokenizer:461] [PID:2204915] [RANK:0] EOS: 128001 / <|end_of_text|>
[2025-05-15 22:52:02,646] [DEBUG] [axolotl.utils.models.load_tokenizer:462] [PID:2204915] [RANK:0] BOS: 128000 / <|begin_of_text|>
[2025-05-15 22:52:02,646] [DEBUG] [axolotl.utils.models.load_tokenizer:463] [PID:2204915] [RANK:0] PAD: 128001 / <|end_of_text|>
[2025-05-15 22:52:02,646] [DEBUG] [axolotl.utils.models.load_tokenizer:464] [PID:2204915] [RANK:0] UNK: None / None
[2025-05-15 22:52:02,646] [INFO] [axolotl.utils.models.load_tokenizer:478] [PID:2204915] [RANK:0] No Chat template selected. Consider adding a chat template for easier inference.
[2025-05-15 22:52:02,646] [DEBUG] [axolotl.train.setup_model_and_tokenizer:77] [PID:2204915] [RANK:0] loading model

Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]
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Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s][2025-05-15 22:52:02,806] [INFO] [axolotl.integrations.liger.pre_model_load:89] [PID:2204915] [RANK:0] Applying LIGER to llama with kwargs: {'rope': True, 'cross_entropy': None, 'fused_linear_cross_entropy': True, 'rms_norm': True, 'swiglu': True}

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[2025-05-15 22:52:05,903] [INFO] [axolotl.utils.models.load_model:1365] [PID:2204918] [RANK:3] Converting modules to torch.bfloat16

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[2025-05-15 22:52:06,037] [INFO] [axolotl.utils.models.load_model:1326] [PID:2204920] [RANK:5] cuda memory usage after model load: 15.000GB (+0.001GB cache, +2.043GB misc)
[2025-05-15 22:52:06,060] [INFO] [axolotl.utils.models.load_model:1365] [PID:2204920] [RANK:5] Converting modules to torch.bfloat16

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[2025-05-15 22:52:06,086] [INFO] [axolotl.utils.models.load_model:1326] [PID:2204921] [RANK:6] cuda memory usage after model load: 15.000GB (+0.001GB cache, +2.043GB misc)
[2025-05-15 22:52:06,106] [INFO] [axolotl.utils.models.load_model:1365] [PID:2204921] [RANK:6] Converting modules to torch.bfloat16

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[2025-05-15 22:52:06,128] [INFO] [axolotl.utils.models.load_model:1326] [PID:2204916] [RANK:1] cuda memory usage after model load: 15.000GB (+0.001GB cache, +2.043GB misc)
[2025-05-15 22:52:06,148] [INFO] [axolotl.utils.models.load_model:1365] [PID:2204916] [RANK:1] Converting modules to torch.bfloat16
[2025-05-15 22:52:06,158] [INFO] [axolotl.utils.models.load_model:1326] [PID:2204917] [RANK:2] cuda memory usage after model load: 15.000GB (+0.001GB cache, +2.043GB misc)
[2025-05-15 22:52:06,179] [INFO] [axolotl.utils.models.load_model:1365] [PID:2204917] [RANK:2] Converting modules to torch.bfloat16
[2025-05-15 22:52:06,187] [INFO] [axolotl.utils.models.load_model:1326] [PID:2204919] [RANK:4] cuda memory usage after model load: 15.000GB (+0.001GB cache, +2.043GB misc)
[2025-05-15 22:52:06,202] [INFO] [axolotl.utils.models.load_model:1326] [PID:2204922] [RANK:7] cuda memory usage after model load: 15.000GB (+0.001GB cache, +2.043GB misc)
[2025-05-15 22:52:06,211] [INFO] [axolotl.utils.models.load_model:1365] [PID:2204919] [RANK:4] Converting modules to torch.bfloat16
[2025-05-15 22:52:06,227] [INFO] [axolotl.utils.models.load_model:1365] [PID:2204922] [RANK:7] Converting modules to torch.bfloat16

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[2025-05-15 22:52:06,599] [INFO] [axolotl.utils.models.load_model:1326] [PID:2204915] [RANK:0] cuda memory usage after model load: 15.000GB (+0.001GB cache, +4.168GB misc)
[2025-05-15 22:52:06,620] [INFO] [axolotl.utils.models.load_model:1365] [PID:2204915] [RANK:0] Converting modules to torch.bfloat16
/home/alexandre/spft/axolotl/src/axolotl/core/trainers/base.py:64: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `AxolotlTrainer.__init__`. Use `processing_class` instead.
  super().__init__(*_args, **kwargs)
[2025-05-15 22:52:06,873] [INFO] [axolotl.integrations.llm_compressor.add_callbacks_post_trainer:143] [PID:2204918] [RANK:3] Adding Sparse Finetuning callback to the trainer
[2025-05-15 22:52:06,873] [INFO] [axolotl.train.save_initial_configs:381] [PID:2204918] [RANK:3] Pre-saving tokenizer to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:07,040] [INFO] [axolotl.train.save_initial_configs:384] [PID:2204918] [RANK:3] Pre-saving model config to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:07,042] [INFO] [axolotl.train.execute_training:215] [PID:2204918] [RANK:3] Starting trainer...
/home/alexandre/spft/axolotl/src/axolotl/core/trainers/base.py:64: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `AxolotlTrainer.__init__`. Use `processing_class` instead.
  super().__init__(*_args, **kwargs)
[2025-05-15 22:52:07,214] [INFO] [axolotl.integrations.llm_compressor.add_callbacks_post_trainer:143] [PID:2204920] [RANK:5] Adding Sparse Finetuning callback to the trainer
[2025-05-15 22:52:07,214] [INFO] [axolotl.train.save_initial_configs:381] [PID:2204920] [RANK:5] Pre-saving tokenizer to Sparse-Llama-3.1-8B-2of4-tldr...
/home/alexandre/spft/axolotl/src/axolotl/core/trainers/base.py:64: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `AxolotlTrainer.__init__`. Use `processing_class` instead.
  super().__init__(*_args, **kwargs)
[2025-05-15 22:52:07,232] [INFO] [axolotl.integrations.llm_compressor.add_callbacks_post_trainer:143] [PID:2204916] [RANK:1] Adding Sparse Finetuning callback to the trainer
[2025-05-15 22:52:07,232] [INFO] [axolotl.train.save_initial_configs:381] [PID:2204916] [RANK:1] Pre-saving tokenizer to Sparse-Llama-3.1-8B-2of4-tldr...
/home/alexandre/spft/axolotl/src/axolotl/core/trainers/base.py:64: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `AxolotlTrainer.__init__`. Use `processing_class` instead.
  super().__init__(*_args, **kwargs)
/home/alexandre/spft/axolotl/src/axolotl/core/trainers/base.py:64: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `AxolotlTrainer.__init__`. Use `processing_class` instead.
  super().__init__(*_args, **kwargs)
[2025-05-15 22:52:07,256] [INFO] [axolotl.integrations.llm_compressor.add_callbacks_post_trainer:143] [PID:2204921] [RANK:6] Adding Sparse Finetuning callback to the trainer
[2025-05-15 22:52:07,256] [INFO] [axolotl.train.save_initial_configs:381] [PID:2204921] [RANK:6] Pre-saving tokenizer to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:07,262] [INFO] [axolotl.integrations.llm_compressor.add_callbacks_post_trainer:143] [PID:2204919] [RANK:4] Adding Sparse Finetuning callback to the trainer
[2025-05-15 22:52:07,262] [INFO] [axolotl.train.save_initial_configs:381] [PID:2204919] [RANK:4] Pre-saving tokenizer to Sparse-Llama-3.1-8B-2of4-tldr...
/home/alexandre/spft/axolotl/src/axolotl/core/trainers/base.py:64: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `AxolotlTrainer.__init__`. Use `processing_class` instead.
  super().__init__(*_args, **kwargs)
[2025-05-15 22:52:07,276] [INFO] [axolotl.integrations.llm_compressor.add_callbacks_post_trainer:143] [PID:2204917] [RANK:2] Adding Sparse Finetuning callback to the trainer
[2025-05-15 22:52:07,276] [INFO] [axolotl.train.save_initial_configs:381] [PID:2204917] [RANK:2] Pre-saving tokenizer to Sparse-Llama-3.1-8B-2of4-tldr...
/home/alexandre/spft/axolotl/src/axolotl/core/trainers/base.py:64: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `AxolotlTrainer.__init__`. Use `processing_class` instead.
  super().__init__(*_args, **kwargs)
[2025-05-15 22:52:07,290] [INFO] [axolotl.integrations.llm_compressor.add_callbacks_post_trainer:143] [PID:2204922] [RANK:7] Adding Sparse Finetuning callback to the trainer
[2025-05-15 22:52:07,290] [INFO] [axolotl.train.save_initial_configs:381] [PID:2204922] [RANK:7] Pre-saving tokenizer to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:07,376] [INFO] [axolotl.train.save_initial_configs:384] [PID:2204920] [RANK:5] Pre-saving model config to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:07,378] [INFO] [axolotl.train.execute_training:215] [PID:2204920] [RANK:5] Starting trainer...
[2025-05-15 22:52:07,397] [INFO] [axolotl.train.save_initial_configs:384] [PID:2204916] [RANK:1] Pre-saving model config to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:07,400] [INFO] [axolotl.train.execute_training:215] [PID:2204916] [RANK:1] Starting trainer...
[2025-05-15 22:52:07,425] [INFO] [axolotl.train.save_initial_configs:384] [PID:2204921] [RANK:6] Pre-saving model config to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:07,428] [INFO] [axolotl.train.execute_training:215] [PID:2204921] [RANK:6] Starting trainer...
[2025-05-15 22:52:07,430] [INFO] [axolotl.train.save_initial_configs:384] [PID:2204919] [RANK:4] Pre-saving model config to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:07,432] [INFO] [axolotl.train.execute_training:215] [PID:2204919] [RANK:4] Starting trainer...
[2025-05-15 22:52:07,459] [INFO] [axolotl.train.save_initial_configs:384] [PID:2204917] [RANK:2] Pre-saving model config to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:07,462] [INFO] [axolotl.train.execute_training:215] [PID:2204917] [RANK:2] Starting trainer...
[2025-05-15 22:52:07,462] [INFO] [axolotl.train.save_initial_configs:384] [PID:2204922] [RANK:7] Pre-saving model config to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:07,465] [INFO] [axolotl.train.execute_training:215] [PID:2204922] [RANK:7] Starting trainer...
/home/alexandre/spft/axolotl/src/axolotl/core/trainers/base.py:64: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `AxolotlTrainer.__init__`. Use `processing_class` instead.
  super().__init__(*_args, **kwargs)
[2025-05-15 22:52:07,932] [INFO] [axolotl.integrations.llm_compressor.add_callbacks_post_trainer:143] [PID:2204915] [RANK:0] Adding Sparse Finetuning callback to the trainer
[2025-05-15 22:52:07,932] [INFO] [axolotl.train.save_initial_configs:381] [PID:2204915] [RANK:0] Pre-saving tokenizer to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:08,097] [INFO] [axolotl.train.save_initial_configs:384] [PID:2204915] [RANK:0] Pre-saving model config to Sparse-Llama-3.1-8B-2of4-tldr...
[2025-05-15 22:52:08,100] [INFO] [axolotl.train.execute_training:215] [PID:2204915] [RANK:0] Starting trainer...
[2025-05-15 22:52:08,618] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:434] [PID:2204915] [RANK:0] gather_len_batches: [10617, 10617, 10617, 10617, 10617, 10617, 10617, 10617]

  0%|          | 0/1327 [00:00<?, ?it/s]Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
[2025-05-15 22:52:09,662] [INFO] [llmcompressor.metrics.logger._create_default_logger:391] [PID:2204915] Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
[2025-05-15 22:52:09,662] [INFO] [llmcompressor.metrics.logger._create_default_logger:391] [PID:2204921] Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
[2025-05-15 22:52:09,662] [INFO] [llmcompressor.metrics.logger._create_default_logger:391] [PID:2204919] Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
[2025-05-15 22:52:09,662] [INFO] [llmcompressor.metrics.logger._create_default_logger:391] [PID:2204920] Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
[2025-05-15 22:52:09,662] [INFO] [llmcompressor.metrics.logger._create_default_logger:391] [PID:2204918] Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
[2025-05-15 22:52:09,662] [INFO] [llmcompressor.metrics.logger._create_default_logger:391] [PID:2204922] Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
[2025-05-15 22:52:09,662] [INFO] [llmcompressor.metrics.logger._create_default_logger:391] [PID:2204917] Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
[2025-05-15 22:52:09,662] [INFO] [llmcompressor.metrics.logger._create_default_logger:391] [PID:2204916] Logging all LLM Compressor modifier-level logs to sparse_logs/15-05-2025_22.52.09.log
manager stage: Modifiers initialized
2025-05-15T22:52:09.779890+0000 | initialize | INFO - Compression lifecycle initialized for 1 modifiers
manager stage: Modifiers initialized
2025-05-15T22:52:09.814666+0000 | initialize | INFO - Compression lifecycle initialized for 1 modifiers
manager stage: Modifiers initialized
2025-05-15T22:52:09.818893+0000 | initialize | INFO - Compression lifecycle initialized for 1 modifiers
manager stage: Modifiers initialized
2025-05-15T22:52:09.823180+0000 | initialize | INFO - Compression lifecycle initialized for 1 modifiers
manager stage: Modifiers initialized
2025-05-15T22:52:09.824964+0000 | initialize | INFO - Compression lifecycle initialized for 1 modifiers
manager stage: Modifiers initialized
2025-05-15T22:52:09.833574+0000 | initialize | INFO - Compression lifecycle initialized for 1 modifiers
manager stage: Modifiers initialized
2025-05-15T22:52:09.837516+0000 | initialize | INFO - Compression lifecycle initialized for 1 modifiers
manager stage: Modifiers initialized
2025-05-15T22:52:09.838847+0000 | initialize | INFO - Compression lifecycle initialized for 1 modifiers
You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.

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{'loss': 2.4149, 'grad_norm': 18.625, 'learning_rate': 0.0, 'epoch': 0.0}

  0%|          | 1/1327 [00:02<59:31,  2.69s/it][2025-05-15 22:52:13,081] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:434] [PID:2204915] [RANK:0] gather_len_batches: [559, 559, 559, 559, 559, 559, 559, 559]


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{'eval_loss': 2.2320923805236816, 'eval_runtime': 48.2606, 'eval_samples_per_second': 120.947, 'eval_steps_per_second': 15.126, 'epoch': 0.0}

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                                               [2025-05-15 22:53:59,153] [INFO] [axolotl.callbacks.on_step_end:128] [PID:2204921] [RANK:6] cuda memory usage while training: 51.986GB (+15.411GB cache, +2.422GB misc)
[2025-05-15 22:53:59,153] [INFO] [axolotl.callbacks.on_step_end:128] [PID:2204919] [RANK:4] cuda memory usage while training: 51.986GB (+15.489GB cache, +2.422GB misc)
[2025-05-15 22:53:59,153] [INFO] [axolotl.callbacks.on_step_end:128] [PID:2204916] [RANK:1] cuda memory usage while training: 51.986GB (+15.489GB cache, +2.422GB misc)
[2025-05-15 22:53:59,154] [INFO] [axolotl.callbacks.on_step_end:128] [PID:2204918] [RANK:3] cuda memory usage while training: 51.986GB (+15.430GB cache, +2.422GB misc)
[2025-05-15 22:53:59,154] [INFO] [axolotl.callbacks.on_step_end:128] [PID:2204920] [RANK:5] cuda memory usage while training: 51.986GB (+15.430GB cache, +2.422GB misc)
[2025-05-15 22:53:59,154] [INFO] [axolotl.callbacks.on_step_end:128] [PID:2204917] [RANK:2] cuda memory usage while training: 51.986GB (+15.509GB cache, +2.422GB misc)
[2025-05-15 22:53:59,155] [INFO] [axolotl.callbacks.on_step_end:128] [PID:2204922] [RANK:7] cuda memory usage while training: 51.986GB (+15.548GB cache, +2.422GB misc)
[2025-05-15 22:53:59,156] [INFO] [axolotl.callbacks.on_step_end:128] [PID:2204915] [RANK:0] cuda memory usage while training: 51.986GB (+15.509GB cache, +4.547GB misc)

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{'loss': 2.2374, 'grad_norm': 18.5, 'learning_rate': 1.5151515151515152e-07, 'epoch': 0.0}

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{'loss': 2.2393, 'grad_norm': 19.125, 'learning_rate': 3.0303030303030305e-07, 'epoch': 0.0}

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{'loss': 2.2044, 'grad_norm': 16.75, 'learning_rate': 4.5454545454545457e-07, 'epoch': 0.0}

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{'loss': 2.3608, 'grad_norm': 18.0, 'learning_rate': 6.060606060606061e-07, 'epoch': 0.0}

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{'loss': 2.2139, 'grad_norm': 19.0, 'learning_rate': 7.575757575757576e-07, 'epoch': 0.0}

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{'loss': 2.461, 'grad_norm': 18.75, 'learning_rate': 9.090909090909091e-07, 'epoch': 0.01}

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{'loss': 2.2565, 'grad_norm': 19.625, 'learning_rate': 1.0606060606060608e-06, 'epoch': 0.01}

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{'loss': 2.2648, 'grad_norm': 19.25, 'learning_rate': 1.2121212121212122e-06, 'epoch': 0.01}

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{'loss': 2.217, 'grad_norm': 18.875, 'learning_rate': 1.3636363636363636e-06, 'epoch': 0.01}

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{'loss': 2.3405, 'grad_norm': 19.125, 'learning_rate': 1.5151515151515152e-06, 'epoch': 0.01}

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{'loss': 2.1533, 'grad_norm': 17.875, 'learning_rate': 1.6666666666666667e-06, 'epoch': 0.01}

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{'loss': 2.1479, 'grad_norm': 17.75, 'learning_rate': 1.8181818181818183e-06, 'epoch': 0.01}

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{'loss': 2.32, 'grad_norm': 18.125, 'learning_rate': 1.96969696969697e-06, 'epoch': 0.01}

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{'loss': 2.2008, 'grad_norm': 19.25, 'learning_rate': 2.1212121212121216e-06, 'epoch': 0.01}

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{'loss': 2.2122, 'grad_norm': 18.125, 'learning_rate': 2.2727272727272728e-06, 'epoch': 0.01}

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{'loss': 2.2271, 'grad_norm': 17.375, 'learning_rate': 2.4242424242424244e-06, 'epoch': 0.01}

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{'loss': 2.1693, 'grad_norm': 17.25, 'learning_rate': 2.575757575757576e-06, 'epoch': 0.01}

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{'loss': 2.2363, 'grad_norm': 17.25, 'learning_rate': 2.7272727272727272e-06, 'epoch': 0.01}

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{'loss': 2.0561, 'grad_norm': 15.875, 'learning_rate': 2.8787878787878793e-06, 'epoch': 0.02}

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{'loss': 2.0329, 'grad_norm': 16.125, 'learning_rate': 3.0303030303030305e-06, 'epoch': 0.02}

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{'loss': 2.2055, 'grad_norm': 15.4375, 'learning_rate': 3.181818181818182e-06, 'epoch': 0.02}

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{'loss': 2.1359, 'grad_norm': 15.0, 'learning_rate': 3.3333333333333333e-06, 'epoch': 0.02}

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{'loss': 2.131, 'grad_norm': 14.5, 'learning_rate': 3.4848484848484854e-06, 'epoch': 0.02}

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{'loss': 1.9887, 'grad_norm': 13.3125, 'learning_rate': 3.6363636363636366e-06, 'epoch': 0.02}

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{'loss': 2.0412, 'grad_norm': 11.8125, 'learning_rate': 3.7878787878787882e-06, 'epoch': 0.02}

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{'loss': 2.0017, 'grad_norm': 9.6875, 'learning_rate': 3.93939393939394e-06, 'epoch': 0.02}

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{'loss': 2.0793, 'grad_norm': 9.6875, 'learning_rate': 4.0909090909090915e-06, 'epoch': 0.02}

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{'loss': 2.0581, 'grad_norm': 8.375, 'learning_rate': 4.242424242424243e-06, 'epoch': 0.02}

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{'loss': 2.031, 'grad_norm': 9.25, 'learning_rate': 4.393939393939394e-06, 'epoch': 0.02}

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{'loss': 1.9376, 'grad_norm': 7.4375, 'learning_rate': 4.5454545454545455e-06, 'epoch': 0.02}

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{'loss': 1.879, 'grad_norm': 6.8125, 'learning_rate': 4.696969696969698e-06, 'epoch': 0.02}

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{'loss': 1.8711, 'grad_norm': 6.53125, 'learning_rate': 4.848484848484849e-06, 'epoch': 0.02}

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{'loss': 1.8815, 'grad_norm': 6.65625, 'learning_rate': 5e-06, 'epoch': 0.03}

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{'loss': 1.9544, 'grad_norm': 5.875, 'learning_rate': 5.151515151515152e-06, 'epoch': 0.03}

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{'loss': 1.9595, 'grad_norm': 5.96875, 'learning_rate': 5.303030303030303e-06, 'epoch': 0.03}

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{'loss': 1.979, 'grad_norm': 5.625, 'learning_rate': 5.4545454545454545e-06, 'epoch': 0.03}

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{'loss': 1.9645, 'grad_norm': 5.90625, 'learning_rate': 5.606060606060606e-06, 'epoch': 0.03}

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{'loss': 1.8439, 'grad_norm': 5.4375, 'learning_rate': 5.7575757575757586e-06, 'epoch': 0.03}

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{'loss': 2.0326, 'grad_norm': 6.09375, 'learning_rate': 5.90909090909091e-06, 'epoch': 0.03}

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{'loss': 1.9237, 'grad_norm': 5.6875, 'learning_rate': 6.060606060606061e-06, 'epoch': 0.03}

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{'loss': 1.8408, 'grad_norm': 5.78125, 'learning_rate': 6.212121212121213e-06, 'epoch': 0.03}

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{'loss': 1.8578, 'grad_norm': 5.71875, 'learning_rate': 6.363636363636364e-06, 'epoch': 0.03}

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{'loss': 1.9115, 'grad_norm': 5.4375, 'learning_rate': 6.515151515151516e-06, 'epoch': 0.03}

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{'loss': 1.9203, 'grad_norm': 5.5, 'learning_rate': 6.666666666666667e-06, 'epoch': 0.03}

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{'loss': 1.9163, 'grad_norm': 5.4375, 'learning_rate': 6.818181818181818e-06, 'epoch': 0.03}

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{'loss': 1.9105, 'grad_norm': 5.25, 'learning_rate': 6.969696969696971e-06, 'epoch': 0.04}

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{'loss': 1.9951, 'grad_norm': 5.4375, 'learning_rate': 7.121212121212122e-06, 'epoch': 0.04}

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{'loss': 1.9055, 'grad_norm': 5.5, 'learning_rate': 7.272727272727273e-06, 'epoch': 0.04}

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{'loss': 1.8735, 'grad_norm': 5.59375, 'learning_rate': 7.424242424242425e-06, 'epoch': 0.04}

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{'loss': 1.9207, 'grad_norm': 5.40625, 'learning_rate': 7.5757575757575764e-06, 'epoch': 0.04}

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{'loss': 1.8536, 'grad_norm': 5.03125, 'learning_rate': 7.727272727272727e-06, 'epoch': 0.04}

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{'loss': 1.907, 'grad_norm': 5.3125, 'learning_rate': 7.87878787878788e-06, 'epoch': 0.04}

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{'loss': 1.9212, 'grad_norm': 5.5625, 'learning_rate': 8.03030303030303e-06, 'epoch': 0.04}

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{'loss': 1.9503, 'grad_norm': 5.5, 'learning_rate': 8.181818181818183e-06, 'epoch': 0.04}

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{'loss': 1.8895, 'grad_norm': 5.0625, 'learning_rate': 8.333333333333334e-06, 'epoch': 0.04}

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{'loss': 1.9265, 'grad_norm': 5.125, 'learning_rate': 8.484848484848486e-06, 'epoch': 0.04}

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{'loss': 1.9021, 'grad_norm': 5.59375, 'learning_rate': 8.636363636363637e-06, 'epoch': 0.04}

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{'loss': 1.9538, 'grad_norm': 5.28125, 'learning_rate': 8.787878787878788e-06, 'epoch': 0.04}

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{'loss': 1.8848, 'grad_norm': 5.09375, 'learning_rate': 8.93939393939394e-06, 'epoch': 0.05}

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{'loss': 1.9475, 'grad_norm': 5.53125, 'learning_rate': 9.090909090909091e-06, 'epoch': 0.05}

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{'loss': 1.8699, 'grad_norm': 5.21875, 'learning_rate': 9.242424242424244e-06, 'epoch': 0.05}

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{'loss': 1.9203, 'grad_norm': 4.875, 'learning_rate': 9.393939393939396e-06, 'epoch': 0.05}

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{'loss': 1.7753, 'grad_norm': 5.03125, 'learning_rate': 9.545454545454547e-06, 'epoch': 0.05}

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{'loss': 1.7838, 'grad_norm': 4.9375, 'learning_rate': 9.696969696969698e-06, 'epoch': 0.05}

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{'loss': 1.8831, 'grad_norm': 4.96875, 'learning_rate': 9.84848484848485e-06, 'epoch': 0.05}

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{'loss': 1.9603, 'grad_norm': 5.15625, 'learning_rate': 1e-05, 'epoch': 0.05}

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{'eval_loss': 1.875820517539978, 'eval_runtime': 54.4544, 'eval_samples_per_second': 107.191, 'eval_steps_per_second': 13.406, 'epoch': 0.05}

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{'loss': 1.856, 'grad_norm': 5.125, 'learning_rate': 9.999984482942972e-06, 'epoch': 0.05}

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{'loss': 1.8677, 'grad_norm': 5.125, 'learning_rate': 9.999937931868196e-06, 'epoch': 0.05}

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{'loss': 1.941, 'grad_norm': 5.40625, 'learning_rate': 9.999860347064607e-06, 'epoch': 0.05}

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{'loss': 1.8349, 'grad_norm': 5.125, 'learning_rate': 9.999751729013761e-06, 'epoch': 0.05}

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{'loss': 1.9128, 'grad_norm': 5.125, 'learning_rate': 9.999612078389829e-06, 'epoch': 0.05}

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{'loss': 1.8743, 'grad_norm': 4.9375, 'learning_rate': 9.998743170388078e-06, 'epoch': 0.06}

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{'loss': 1.8892, 'grad_norm': 5.21875, 'learning_rate': 9.998122552633702e-06, 'epoch': 0.06}

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{'loss': 1.8826, 'grad_norm': 5.0625, 'learning_rate': 9.997765709053744e-06, 'epoch': 0.06}

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{'loss': 1.8362, 'grad_norm': 5.09375, 'learning_rate': 9.997377845227577e-06, 'epoch': 0.06}

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{'loss': 1.8628, 'grad_norm': 5.09375, 'learning_rate': 9.996958963562598e-06, 'epoch': 0.06}

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{'loss': 1.7405, 'grad_norm': 4.8125, 'learning_rate': 9.996509066658738e-06, 'epoch': 0.06}

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{'loss': 1.9461, 'grad_norm': 5.21875, 'learning_rate': 9.996028157308422e-06, 'epoch': 0.06}

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{'loss': 1.9174, 'grad_norm': 5.46875, 'learning_rate': 9.995516238496571e-06, 'epoch': 0.06}

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{'loss': 1.7908, 'grad_norm': 4.96875, 'learning_rate': 9.994973313400577e-06, 'epoch': 0.06}

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{'loss': 1.9807, 'grad_norm': 5.28125, 'learning_rate': 9.993158535068244e-06, 'epoch': 0.07}

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{'loss': 1.9017, 'grad_norm': 5.1875, 'learning_rate': 9.992491620458251e-06, 'epoch': 0.07}

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{'loss': 1.8628, 'grad_norm': 5.15625, 'learning_rate': 9.99179371833738e-06, 'epoch': 0.07}

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{'loss': 1.859, 'grad_norm': 5.15625, 'learning_rate': 9.991064833037386e-06, 'epoch': 0.07}

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{'loss': 1.82, 'grad_norm': 4.9375, 'learning_rate': 9.990304969082332e-06, 'epoch': 0.07}

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{'loss': 1.8503, 'grad_norm': 5.1875, 'learning_rate': 9.98951413118856e-06, 'epoch': 0.07}

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{'loss': 1.8717, 'grad_norm': 5.125, 'learning_rate': 9.988692324264657e-06, 'epoch': 0.07}

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{'loss': 1.7455, 'grad_norm': 4.90625, 'learning_rate': 9.987839553411435e-06, 'epoch': 0.07}

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{'loss': 1.8897, 'grad_norm': 5.25, 'learning_rate': 9.986955823921895e-06, 'epoch': 0.07}

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{'loss': 1.9899, 'grad_norm': 5.21875, 'learning_rate': 9.984118939447432e-06, 'epoch': 0.07}

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{'loss': 1.8912, 'grad_norm': 5.25, 'learning_rate': 9.977610088314706e-06, 'epoch': 0.08}

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{'loss': 1.7882, 'grad_norm': 5.0625, 'learning_rate': 9.97641710583307e-06, 'epoch': 0.08}

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{'loss': 1.8081, 'grad_norm': 5.09375, 'learning_rate': 9.95969438192774e-06, 'epoch': 0.09}

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{'loss': 1.9093, 'grad_norm': 5.28125, 'learning_rate': 9.944242505019424e-06, 'epoch': 0.1}

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{'loss': 1.8749, 'grad_norm': 5.21875, 'learning_rate': 9.942372041814108e-06, 'epoch': 0.1}

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{'loss': 1.7506, 'grad_norm': 4.75, 'learning_rate': 9.940470902181259e-06, 'epoch': 0.1}

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{'loss': 1.8752, 'grad_norm': 5.46875, 'learning_rate': 9.938539097920912e-06, 'epoch': 0.1}

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{'loss': 1.8645, 'grad_norm': 5.375, 'learning_rate': 9.936576641023437e-06, 'epoch': 0.1}

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{'loss': 1.9723, 'grad_norm': 5.375, 'learning_rate': 9.934583543669454e-06, 'epoch': 0.1}

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{'loss': 1.8308, 'grad_norm': 5.875, 'learning_rate': 9.932559818229766e-06, 'epoch': 0.1}

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{'loss': 1.8909, 'grad_norm': 5.375, 'learning_rate': 9.93050547726528e-06, 'epoch': 0.1}

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{'eval_loss': 1.8560287952423096, 'eval_runtime': 54.3735, 'eval_samples_per_second': 107.35, 'eval_steps_per_second': 13.426, 'epoch': 0.1}

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{'loss': 1.8313, 'grad_norm': 5.03125, 'learning_rate': 9.926304999955572e-06, 'epoch': 0.1}

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{'loss': 1.7287, 'grad_norm': 4.875, 'learning_rate': 9.921982216026644e-06, 'epoch': 0.1}

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{'loss': 1.902, 'grad_norm': 5.03125, 'learning_rate': 9.919774992499819e-06, 'epoch': 0.1}

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{'eval_loss': 1.8490650653839111, 'eval_runtime': 53.9551, 'eval_samples_per_second': 108.182, 'eval_steps_per_second': 13.53, 'epoch': 0.15}

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{'eval_loss': 1.8440780639648438, 'eval_runtime': 54.2836, 'eval_samples_per_second': 107.528, 'eval_steps_per_second': 13.448, 'epoch': 0.2}

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{'eval_loss': 1.8410518169403076, 'eval_runtime': 54.4251, 'eval_samples_per_second': 107.248, 'eval_steps_per_second': 13.413, 'epoch': 0.25}

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{'eval_loss': 1.83809494972229, 'eval_runtime': 55.1776, 'eval_samples_per_second': 105.786, 'eval_steps_per_second': 13.23, 'epoch': 0.3}

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{'eval_loss': 1.8359923362731934, 'eval_runtime': 54.8821, 'eval_samples_per_second': 106.355, 'eval_steps_per_second': 13.301, 'epoch': 0.35}

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{'eval_loss': 1.8341656923294067, 'eval_runtime': 53.515, 'eval_samples_per_second': 109.072, 'eval_steps_per_second': 13.641, 'epoch': 0.4}

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{'eval_loss': 1.8328338861465454, 'eval_runtime': 54.4646, 'eval_samples_per_second': 107.171, 'eval_steps_per_second': 13.403, 'epoch': 0.45}

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{'loss': 1.7336, 'grad_norm': 4.78125, 'learning_rate': 5.367143431823409e-06, 'epoch': 0.5}

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{'loss': 1.8171, 'grad_norm': 4.96875, 'learning_rate': 5.342292729385459e-06, 'epoch': 0.5}

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{'eval_loss': 1.8317328691482544, 'eval_runtime': 53.7844, 'eval_samples_per_second': 108.526, 'eval_steps_per_second': 13.573, 'epoch': 0.5}

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{'eval_loss': 1.8309379816055298, 'eval_runtime': 54.5547, 'eval_samples_per_second': 106.993, 'eval_steps_per_second': 13.381, 'epoch': 0.56}

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{'eval_loss': 1.8302642107009888, 'eval_runtime': 54.7448, 'eval_samples_per_second': 106.622, 'eval_steps_per_second': 13.335, 'epoch': 0.61}

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{'loss': 1.7998, 'grad_norm': 4.71875, 'learning_rate': 2.916746276833655e-06, 'epoch': 0.66}

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{'eval_loss': 1.8298438787460327, 'eval_runtime': 54.1188, 'eval_samples_per_second': 107.855, 'eval_steps_per_second': 13.489, 'epoch': 0.66}

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{'loss': 1.7743, 'grad_norm': 5.0, 'learning_rate': 2.1802148507454675e-06, 'epoch': 0.71}

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{'eval_loss': 1.8295960426330566, 'eval_runtime': 54.6822, 'eval_samples_per_second': 106.744, 'eval_steps_per_second': 13.35, 'epoch': 0.71}

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{'eval_loss': 1.8295001983642578, 'eval_runtime': 53.9484, 'eval_samples_per_second': 108.196, 'eval_steps_per_second': 13.531, 'epoch': 0.76}

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{'eval_loss': 1.8294135332107544, 'eval_runtime': 53.6406, 'eval_samples_per_second': 108.817, 'eval_steps_per_second': 13.609, 'epoch': 0.81}

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{'loss': 1.9079, 'grad_norm': 5.4375, 'learning_rate': 5.497843243051715e-07, 'epoch': 0.86}

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{'loss': 1.9432, 'grad_norm': 5.4375, 'learning_rate': 5.441194031529218e-07, 'epoch': 0.86}

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{'eval_loss': 1.8294780254364014, 'eval_runtime': 54.5933, 'eval_samples_per_second': 106.918, 'eval_steps_per_second': 13.372, 'epoch': 0.86}

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{'loss': 1.89, 'grad_norm': 5.09375, 'learning_rate': 2.3669890588012723e-07, 'epoch': 0.91}

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{'loss': 1.8066, 'grad_norm': 5.25, 'learning_rate': 2.329263788082675e-07, 'epoch': 0.91}

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{'loss': 1.7789, 'grad_norm': 5.0625, 'learning_rate': 2.291834401177051e-07, 'epoch': 0.91}

 91%|█████████ | 1206/1327 [50:37<01:59,  1.01it/s][2025-05-15 23:42:47,728] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:434] [PID:2204915] [RANK:0] gather_len_batches: [559, 559, 559, 559, 559, 559, 559, 559]


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{'eval_loss': 1.8293883800506592, 'eval_runtime': 54.7671, 'eval_samples_per_second': 106.579, 'eval_steps_per_second': 13.329, 'epoch': 0.91}

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{'eval_loss': 1.8294706344604492, 'eval_runtime': 54.6991, 'eval_samples_per_second': 106.711, 'eval_steps_per_second': 13.346, 'epoch': 0.96}

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{'train_runtime': 3312.9967, 'train_samples_per_second': 33.47, 'train_steps_per_second': 0.401, 'train_loss': 1.8447747308685085, 'epoch': 1.0}

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manager stage: Modifiers finalized
manager stage: Modifiers finalized
manager stage: Modifiers finalized
2025-05-15T23:47:22.625358+0000 | finalize | INFO - Compression lifecycle finalized for 1 modifiers
manager stage: Modifiers finalized
manager stage: Modifiers finalized
manager stage: Modifiers finalized
2025-05-15T23:47:22.625467+0000 | finalize | INFO - Compression lifecycle finalized for 1 modifiers
[2025-05-15 23:47:22,625] [INFO] [axolotl.train.save_trained_model:234] [PID:2204916] [RANK:1] Training completed! Saving pre-trained model to Sparse-Llama-3.1-8B-2of4-tldr.
2025-05-15T23:47:22.625529+0000 | finalize | INFO - Compression lifecycle finalized for 1 modifiers
manager stage: Modifiers finalized
manager stage: Modifiers finalized
[2025-05-15 23:47:22,625] [INFO] [axolotl.train.save_trained_model:234] [PID:2204917] [RANK:2] Training completed! Saving pre-trained model to Sparse-Llama-3.1-8B-2of4-tldr.
2025-05-15T23:47:22.625603+0000 | finalize | INFO - Compression lifecycle finalized for 1 modifiers
2025-05-15T23:47:22.625643+0000 | finalize | INFO - Compression lifecycle finalized for 1 modifiers
2025-05-15T23:47:22.625677+0000 | finalize | INFO - Compression lifecycle finalized for 1 modifiers
[2025-05-15 23:47:22,625] [INFO] [axolotl.train.save_trained_model:234] [PID:2204915] [RANK:0] Training completed! Saving pre-trained model to Sparse-Llama-3.1-8B-2of4-tldr.
[2025-05-15 23:47:22,625] [INFO] [axolotl.train.save_trained_model:234] [PID:2204918] [RANK:3] Training completed! Saving pre-trained model to Sparse-Llama-3.1-8B-2of4-tldr.
[2025-05-15 23:47:22,625] [INFO] [axolotl.train.save_trained_model:234] [PID:2204919] [RANK:4] Training completed! Saving pre-trained model to Sparse-Llama-3.1-8B-2of4-tldr.
2025-05-15T23:47:22.625790+0000 | finalize | INFO - Compression lifecycle finalized for 1 modifiers
[2025-05-15 23:47:22,625] [INFO] [axolotl.train.save_trained_model:234] [PID:2204920] [RANK:5] Training completed! Saving pre-trained model to Sparse-Llama-3.1-8B-2of4-tldr.
2025-05-15T23:47:22.625820+0000 | finalize | INFO - Compression lifecycle finalized for 1 modifiers
[2025-05-15 23:47:22,626] [INFO] [axolotl.train.save_trained_model:234] [PID:2204922] [RANK:7] Training completed! Saving pre-trained model to Sparse-Llama-3.1-8B-2of4-tldr.
[2025-05-15 23:47:22,626] [INFO] [axolotl.train.save_trained_model:234] [PID:2204921] [RANK:6] Training completed! Saving pre-trained model to Sparse-Llama-3.1-8B-2of4-tldr.
2025-05-15T23:47:58.869142+0000 | save_pretrained_wrapper | INFO - Fetching state_dict - this may take some time
2025-05-15T23:48:34.695753+0000 | save_pretrained_wrapper | INFO - Fetching compressor

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Calculating model sparsity:  66%|██████▋   | 193/291 [00:22<00:08, 11.51it/s]
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Calculating model sparsity: 100%|██████████| 291/291 [00:36<00:00,  3.08it/s]
Calculating model sparsity: 100%|██████████| 291/291 [00:36<00:00,  8.05it/s]

Checking whether model follows 2:4 sparsity structure:   0%|          | 0/225 [00:00<?, ?it/s]
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Checking whether model follows 2:4 sparsity structure:  56%|█████▌    | 125/225 [00:23<00:20,  4.88it/s]
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Checking whether model follows 2:4 sparsity structure:  78%|███████▊  | 176/225 [00:33<00:10,  4.86it/s]
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Checking whether model follows 2:4 sparsity structure: 100%|██████████| 225/225 [00:45<00:00,  1.13it/s]
Checking whether model follows 2:4 sparsity structure: 100%|██████████| 225/225 [00:45<00:00,  4.97it/s]
2025-05-15T23:50:31.194248+0000 | is_sparse24_bitmask_supported | WARNING - Compressed Sparse-only 2:4 models are not supported in vLLM<=0.7.0, consider saving with `disable_sparse_compression` set, `model.save_pretrained(..., disable_sparse_compression=True)`

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2025-05-15T23:54:01.904272+0000 | save_pretrained_wrapper | INFO - Saving compressed model to disk
CLEARML-SERVER new package available: UPGRADE to v2.0.0 is recommended!
Release Notes:
### Breaking Changes

MongoDB major version was upgraded from v5.x to 6.x.
Please note that if your current ClearML Server version is smaller than v1.17 (where MongoDB v5.x was first used), you'll need to first upgrade to ClearML Server v1.17.
#### Upgrading to ClearML Server v1.17 from a previous version
- If using docker-compose,  use the following docker-compose files:
  * [docker-compose file](https://github.com/allegroai/clearml-server/blob/2976ce69cc91550a3614996e8a8d8cd799af2efd/upgrade/1_17_to_2_0/docker-compose.yml)
  * [docker-compose file foe Windows](https://github.com/allegroai/clearml-server/blob/2976ce69cc91550a3614996e8a8d8cd799af2efd/upgrade/1_17_to_2_0/docker-compose-win10.yml)

### New Features

- New look and feel: Full light/dark themes ([clearml #1297](https://github.com/allegroai/clearml/issues/1297))
- New UI task creation options
  - Support bash as well as python scripts
  - Support file upload
- New UI setting for configuring cloud storage credentials with which ClearML can clean up cloud storage artifacts on task deletion. 
- Add UI scalar plots presentation of plots in sections grouped by metrics.
- Add UI Batch export plot embed codes for all metric plots in a single click.
- Add UI pipeline presentation of steps grouped into stages

### Bug Fixes
- Fix UI Model Endpoint's Number of Requests plot sometimes displays incorrect data
- Fix UI datasets page does not filter according to project when dataset is running 
- Fix UI task scalar legend does not change colors when smoothing is enabled 
- Fix queue list in UI Workers and Queues page does not alphabetically sort by queue display name 
- Fix queue display name is not searchable in UI Task Creation modal's queue field

ClearML results page: http://clearml.wisp.internal.neuralmagic.com/projects/2ba7f277eeeb4c9cad8760b63a328c7b/experiments/c1393538e4394832aeb1fbefd1138499/output/log