OSError: 0.1.0-small is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'

[C0casio45]

🐛 Describe the bug

I use the base example extract pdf

• I am using nvcr.io/nvidia/pytorch:24.07-py3 docker container
• I have installed last Anaconda version
• I have a OPEN AI key
• I am logged in huggingface (getting an other error if iI try with mistral for example)

Versions

PyTorch version: 2.5.0.dev20240724+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.30.0 Libc version: glibc-2.35

Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-113-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.5.82 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA H100 PCIe Nvidia driver version: 550.90.07 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.2.1 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 24 On-line CPU(s) list: 0-23 Vendor ID: AuthenticAMD Model name: AMD EPYC 9334 32-Core Processor CPU family: 25 Model: 17 Thread(s) per core: 1 Core(s) per socket: 24 Socket(s): 1 Stepping: 1 BogoMIPS: 5399.98 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw perfctr_core invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr wbnoinvd arat npt lbrv nrip_save tsc_scale vmcb_clean pausefilter pfthreshold v_vmsave_vmload vgif avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid fsrm arch_capabilities Virtualization: AMD-V Hypervisor vendor: KVM Virtualization type: full L1d cache: 1.5 MiB (24 instances) L1i cache: 1.5 MiB (24 instances) L2 cache: 12 MiB (24 instances) L3 cache: 384 MiB (24 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-23 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Mitigation; safe RET Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] pytorch-triton==3.0.0+dedb7bdf33 [pip3] torch==2.5.0.dev20240724+cu121 [conda] pytorch-triton 3.0.0+dedb7bdf33 pypi_0 pypi [conda] torch 2.5.0.dev20240724+cu121 pypi_0 pypi