Closed
Description
🐛 Describe the bug
From the README, its not very clear how to download different flavor/sizes of the models from HF, unless someone go to the next section and find the inventory list https://github.com/pytorch/torchchat#download-weights
might be helpful to add the inventory list command upper before the the download command.
Also as we have 3.2 it would be great to update the docs.
/torchchat$ python3 torchchat.py list
Model Aliases Downloaded
-------------------------------------------- ---------------------------------------------------------- -----------
meta-llama/llama-2-7b-hf llama2-base, llama2-7b
meta-llama/llama-2-7b-chat-hf llama2, llama2-chat, llama2-7b-chat
meta-llama/llama-2-13b-chat-hf llama2-13b-chat
meta-llama/llama-2-70b-chat-hf llama2-70b-chat
meta-llama/meta-llama-3-8b llama3-base
meta-llama/meta-llama-3-8b-instruct llama3, llama3-chat, llama3-instruct Yes
meta-llama/meta-llama-3-70b-instruct llama3-70b
meta-llama/meta-llama-3.1-8b llama3.1-base
meta-llama/meta-llama-3.1-8b-instruct llama3.1, llama3.1-chat, llama3.1-instruct
meta-llama/meta-llama-3.1-70b-instruct llama3.1-70b
meta-llama/meta-llama-3.1-8b-instruct-tune llama3.1-tune, llama3.1-chat-tune, llama3.1-instruct-tune
meta-llama/meta-llama-3.1-70b-instruct-tune llama3.1-70b-tune
meta-llama/meta-llama-3.2-1b llama3.2-1b-base
meta-llama/meta-llama-3.2-1b-instruct llama3.2-1b, llama3.2-1b-chat, llama3.2-1b-instruct
meta-llama/llama-guard-3-1b llama3-1b-guard, llama3.2-1b-guard
meta-llama/meta-llama-3.2-3b llama3.2-3b-base
meta-llama/meta-llama-3.2-3b-instruct llama3.2-3b, llama3.2-3b-chat, llama3.2-3b-instruct
meta-llama/llama-3.2-11b-vision llama3.2-11B-base, Llama-3.2-11B-Vision-base
meta-llama/llama-3.2-11b-vision-instruct llama3.2-11B, Llama-3.2-11B-Vision, Llama-3.2-mm
meta-llama/codellama-7b-python-hf codellama, codellama-7b
meta-llama/codellama-34b-python-hf codellama-34b
mistralai/mistral-7b-v0.1 mistral-7b-v01-base
mistralai/mistral-7b-instruct-v0.1 mistral-7b-v01-instruct
mistralai/mistral-7b-instruct-v0.2 mistral, mistral-7b, mistral-7b-instruct
openlm-research/open_llama_7b open-llama, open-llama-7b
stories15m
stories42m
stories110m
Versions
Collecting environment information...
PyTorch version: 2.5.0.dev20240901+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.30.3
Libc version: glibc-2.31
Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-1068-aws-x86_64-with-glibc2.31
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
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
Byte Order: Little Endian
Address sizes: 46 bits physical, 48 bits virtual
CPU(s): 16
On-line CPU(s) list: 0-15
Thread(s) per core: 2
Core(s) per socket: 8
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 143
Model name: Intel(R) Xeon(R) Platinum 8488C
Stepping: 8
CPU MHz: 2400.000
BogoMIPS: 4800.00
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 384 KiB
L1i cache: 256 KiB
L2 cache: 16 MiB
L3 cache: 105 MiB
NUMA node0 CPU(s): 0-15
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 Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced 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 avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorch-triton==3.0.0+dedb7bdf33
[pip3] torch==2.5.0.dev20240901+cu121
[pip3] torchao==0.5.0
[pip3] torchtune==0.0.0
[pip3] torchvision==0.20.0.dev20240901+cu121
[conda] numpy 1.26.4 pypi_0 pypi
[conda] pytorch-triton 3.0.0+dedb7bdf33 pypi_0 pypi
[conda] torch 2.5.0.dev20240901+cu121 pypi_0 pypi
[conda] torchao 0.5.0 pypi_0 pypi
[conda] torchtune 0.0.0 pypi_0 pypi
[conda] torchvision 0.20.0.dev20240901+cu121 pypi_0 pypi