Closed bojak83318 closed 5 months ago
my rig with 2 7900 XTX:
super@super-System-Product-Name:~/apps/llama.cpp$ rocm-smi
========================= ROCm System Management Interface =========================
=================================== Concise Info ===================================
ERROR: GPU[2] : sclk clock is unsupported
====================================================================================
GPU[2] : get_power_cap, Not supported on the given system
GPU Temp (DieEdge) AvgPwr SCLK MCLK Fan Perf PwrCap VRAM% GPU%
0 33.0c 24.0W 295Mhz 96Mhz 0% auto 339.0W 0% 10%
1 31.0c 81.0W 1514Mhz 96Mhz 0% auto 339.0W 0% 56%
2 37.0c 55.141W None 1800Mhz 0% auto Unsupported 9% 0%
====================================================================================
=============================== End of ROCm SMI Log ================================
super@super-System-Product-Name:~/apps/llama.cpp$ rocminfo
ROCk module is loaded
=====================
HSA System Attributes
=====================
Runtime Version: 1.1
System Timestamp Freq.: 1000.000000MHz
Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
Machine Model: LARGE
System Endianness: LITTLE
Mwaitx: DISABLED
DMAbuf Support: YES
==========
HSA Agents
==========
*******
Agent 1
*******
Name: AMD Ryzen 9 7950X3D 16-Core Processor
Uuid: CPU-XX
Marketing Name: AMD Ryzen 9 7950X3D 16-Core Processor
Vendor Name: CPU
Feature: None specified
Profile: FULL_PROFILE
Float Round Mode: NEAR
Max Queue Number: 0(0x0)
Queue Min Size: 0(0x0)
Queue Max Size: 0(0x0)
Queue Type: MULTI
Node: 0
Device Type: CPU
Cache Info:
L1: 32768(0x8000) KB
Chip ID: 0(0x0)
ASIC Revision: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 4200
BDFID: 0
Internal Node ID: 0
Compute Unit: 32
SIMDs per CU: 0
Shader Engines: 0
Shader Arrs. per Eng.: 0
WatchPts on Addr. Ranges:1
Features: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: FINE GRAINED
Size: 131082728(0x7d029e8) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 2
Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED
Size: 131082728(0x7d029e8) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 3
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 131082728(0x7d029e8) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
ISA Info:
*******
Agent 2
*******
Name: gfx1100
Uuid: GPU-aee456bdb1c699e6
Marketing Name: Radeon RX 7900 XTX
Vendor Name: AMD
Feature: KERNEL_DISPATCH
Profile: BASE_PROFILE
Float Round Mode: NEAR
Max Queue Number: 128(0x80)
Queue Min Size: 64(0x40)
Queue Max Size: 131072(0x20000)
Queue Type: MULTI
Node: 1
Device Type: GPU
Cache Info:
L1: 32(0x20) KB
L2: 6144(0x1800) KB
L3: 98304(0x18000) KB
Chip ID: 29772(0x744c)
ASIC Revision: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 2526
BDFID: 768
Internal Node ID: 1
Compute Unit: 96
SIMDs per CU: 2
Shader Engines: 6
Shader Arrs. per Eng.: 2
WatchPts on Addr. Ranges:4
Features: KERNEL_DISPATCH
Fast F16 Operation: TRUE
Wavefront Size: 32(0x20)
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Max Waves Per CU: 32(0x20)
Max Work-item Per CU: 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
Max fbarriers/Workgrp: 32
Packet Processor uCode:: 546
SDMA engine uCode:: 19
IOMMU Support:: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 25149440(0x17fc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 2
Segment: GLOBAL; FLAGS:
Size: 25149440(0x17fc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 3
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx1100
Machine Models: HSA_MACHINE_MODEL_LARGE
Profiles: HSA_PROFILE_BASE
Default Rounding Mode: NEAR
Default Rounding Mode: NEAR
Fast f16: TRUE
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
FBarrier Max Size: 32
*******
Agent 3
*******
Name: gfx1100
Uuid: GPU-398a3f843a146602
Marketing Name: Radeon RX 7900 XTX
Vendor Name: AMD
Feature: KERNEL_DISPATCH
Profile: BASE_PROFILE
Float Round Mode: NEAR
Max Queue Number: 128(0x80)
Queue Min Size: 64(0x40)
Queue Max Size: 131072(0x20000)
Queue Type: MULTI
Node: 2
Device Type: GPU
Cache Info:
L1: 32(0x20) KB
L2: 6144(0x1800) KB
L3: 98304(0x18000) KB
Chip ID: 29772(0x744c)
ASIC Revision: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 2526
BDFID: 2304
Internal Node ID: 2
Compute Unit: 96
SIMDs per CU: 2
Shader Engines: 6
Shader Arrs. per Eng.: 2
WatchPts on Addr. Ranges:4
Features: KERNEL_DISPATCH
Fast F16 Operation: TRUE
Wavefront Size: 32(0x20)
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Max Waves Per CU: 32(0x20)
Max Work-item Per CU: 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
Max fbarriers/Workgrp: 32
Packet Processor uCode:: 546
SDMA engine uCode:: 19
IOMMU Support:: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 25149440(0x17fc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 2
Segment: GLOBAL; FLAGS:
Size: 25149440(0x17fc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 3
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx1100
Machine Models: HSA_MACHINE_MODEL_LARGE
Profiles: HSA_PROFILE_BASE
Default Rounding Mode: NEAR
Default Rounding Mode: NEAR
Fast f16: TRUE
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
FBarrier Max Size: 32
*******
Agent 4
*******
Name: gfx1036
Uuid: GPU-XX
Marketing Name: AMD Radeon Graphics
Vendor Name: AMD
Feature: KERNEL_DISPATCH
Profile: BASE_PROFILE
Float Round Mode: NEAR
Max Queue Number: 128(0x80)
Queue Min Size: 64(0x40)
Queue Max Size: 131072(0x20000)
Queue Type: MULTI
Node: 3
Device Type: GPU
Cache Info:
L1: 16(0x10) KB
L2: 256(0x100) KB
Chip ID: 5710(0x164e)
ASIC Revision: 1(0x1)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 2200
BDFID: 4352
Internal Node ID: 3
Compute Unit: 2
SIMDs per CU: 2
Shader Engines: 1
Shader Arrs. per Eng.: 1
WatchPts on Addr. Ranges:4
Features: KERNEL_DISPATCH
Fast F16 Operation: TRUE
Wavefront Size: 32(0x20)
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Max Waves Per CU: 32(0x20)
Max Work-item Per CU: 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
Max fbarriers/Workgrp: 32
Packet Processor uCode:: 20
SDMA engine uCode:: 8
IOMMU Support:: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 524288(0x80000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 2
Segment: GLOBAL; FLAGS:
Size: 524288(0x80000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 3
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx1036
Machine Models: HSA_MACHINE_MODEL_LARGE
Profiles: HSA_PROFILE_BASE
Default Rounding Mode: NEAR
Default Rounding Mode: NEAR
Fast f16: TRUE
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
FBarrier Max Size: 32
*** Done ***
super@super-System-Product-Name:~/apps/llama.cpp$ HIP_VISIBLE_DEVICES=0 ./bin/main -m ../models/openbuddy-mistral-7b-v13.1.Q8_0.gguf -
n 128 -ngl 50
Log start
main: build = 1499 (875fb42)
main: built with AMD clang version 17.0.0 (https://github.com/RadeonOpenCompute/llvm-project roc-5.7.0 23352 d1e13c532a947d0cbfc94759c00dcf152294aa13) for x86_64-unknown-linux-gnu
main: seed = 1699491182
ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 1 ROCm devices:
Device 0: Radeon RX 7900 XTX, compute capability 11.0
llama_model_loader: loaded meta data with 20 key-value pairs and 291 tensors from ../models/openbuddy-mistral-7b-v13.1.Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: - tensor 0: token_embd.weight q8_0 [ 4096, 36608, 1, 1 ]
llama_model_loader: - tensor 1: blk.0.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 2: blk.0.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 3: blk.0.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 4: blk.0.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 5: blk.0.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 6: blk.0.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 7: blk.0.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 8: blk.0.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 9: blk.0.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 10: blk.1.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 11: blk.1.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 12: blk.1.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 13: blk.1.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 14: blk.1.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 15: blk.1.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 16: blk.1.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 17: blk.1.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 18: blk.1.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 19: blk.2.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 20: blk.2.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 21: blk.2.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 22: blk.2.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 23: blk.2.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 24: blk.2.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 25: blk.2.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 26: blk.2.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 27: blk.2.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 28: blk.3.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 29: blk.3.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 30: blk.3.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 31: blk.3.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 32: blk.3.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 33: blk.3.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 34: blk.3.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 35: blk.3.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 36: blk.3.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 37: blk.4.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 38: blk.4.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 39: blk.4.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 40: blk.4.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 41: blk.4.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 42: blk.4.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 43: blk.4.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 44: blk.4.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 45: blk.4.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 46: blk.5.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 47: blk.5.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 48: blk.5.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 49: blk.5.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 50: blk.5.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 51: blk.5.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 52: blk.5.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 53: blk.5.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 54: blk.5.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 55: blk.6.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 56: blk.6.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 57: blk.6.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 58: blk.6.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 59: blk.6.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 60: blk.6.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 61: blk.6.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 62: blk.6.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 63: blk.6.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 64: blk.7.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 65: blk.7.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 66: blk.7.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 67: blk.7.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 68: blk.7.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 69: blk.7.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 70: blk.7.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 71: blk.7.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 72: blk.7.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 73: blk.8.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 74: blk.8.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 75: blk.8.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 76: blk.8.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 77: blk.8.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 78: blk.8.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 79: blk.8.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 80: blk.8.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 81: blk.8.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 82: blk.9.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 83: blk.9.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 84: blk.9.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 85: blk.9.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 86: blk.9.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 87: blk.9.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 88: blk.9.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 89: blk.9.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 90: blk.9.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 91: blk.10.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 92: blk.10.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 93: blk.10.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 94: blk.10.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 95: blk.10.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 96: blk.10.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 97: blk.10.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 98: blk.10.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 99: blk.10.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 100: blk.11.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 101: blk.11.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 102: blk.11.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 103: blk.11.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 104: blk.11.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 105: blk.11.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 106: blk.11.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 107: blk.11.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 108: blk.11.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 109: blk.12.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 110: blk.12.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 111: blk.12.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 112: blk.12.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 113: blk.12.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 114: blk.12.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 115: blk.12.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 116: blk.12.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 117: blk.12.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 118: blk.13.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 119: blk.13.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 120: blk.13.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 121: blk.13.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 122: blk.13.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 123: blk.13.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 124: blk.13.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 125: blk.13.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 126: blk.13.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 127: blk.14.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 128: blk.14.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 129: blk.14.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 130: blk.14.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 131: blk.14.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 132: blk.14.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 133: blk.14.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 134: blk.14.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 135: blk.14.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 136: blk.15.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 137: blk.15.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 138: blk.15.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 139: blk.15.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 140: blk.15.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 141: blk.15.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 142: blk.15.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 143: blk.15.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 144: blk.15.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 145: blk.16.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 146: blk.16.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 147: blk.16.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 148: blk.16.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 149: blk.16.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 150: blk.16.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 151: blk.16.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 152: blk.16.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 153: blk.16.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 154: blk.17.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 155: blk.17.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 156: blk.17.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 157: blk.17.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 158: blk.17.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 159: blk.17.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 160: blk.17.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 161: blk.17.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 162: blk.17.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 163: blk.18.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 164: blk.18.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 165: blk.18.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 166: blk.18.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 167: blk.18.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 168: blk.18.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 169: blk.18.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 170: blk.18.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 171: blk.18.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 172: blk.19.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 173: blk.19.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 174: blk.19.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 175: blk.19.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 176: blk.19.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 177: blk.19.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 178: blk.19.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 179: blk.19.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 180: blk.19.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 181: blk.20.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 182: blk.20.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 183: blk.20.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 184: blk.20.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 185: blk.20.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 186: blk.20.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 187: blk.20.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 188: blk.20.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 189: blk.20.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 190: blk.21.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 191: blk.21.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 192: blk.21.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 193: blk.21.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 194: blk.21.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 195: blk.21.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 196: blk.21.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 197: blk.21.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 198: blk.21.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 199: blk.22.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 200: blk.22.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 201: blk.22.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 202: blk.22.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 203: blk.22.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 204: blk.22.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 205: blk.22.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 206: blk.22.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 207: blk.22.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 208: blk.23.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 209: blk.23.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 210: blk.23.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 211: blk.23.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 212: blk.23.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 213: blk.23.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 214: blk.23.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 215: blk.23.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 216: blk.23.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 217: blk.24.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 218: blk.24.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 219: blk.24.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 220: blk.24.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 221: blk.24.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 222: blk.24.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 223: blk.24.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 224: blk.24.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 225: blk.24.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 226: blk.25.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 227: blk.25.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 228: blk.25.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 229: blk.25.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 230: blk.25.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 231: blk.25.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 232: blk.25.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 233: blk.25.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 234: blk.25.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 235: blk.26.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 236: blk.26.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 237: blk.26.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 238: blk.26.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 239: blk.26.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 240: blk.26.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 241: blk.26.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 242: blk.26.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 243: blk.26.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 244: blk.27.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 245: blk.27.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 246: blk.27.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 247: blk.27.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 248: blk.27.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 249: blk.27.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 250: blk.27.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 251: blk.27.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 252: blk.27.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 253: blk.28.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 254: blk.28.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 255: blk.28.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 256: blk.28.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 257: blk.28.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 258: blk.28.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 259: blk.28.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 260: blk.28.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 261: blk.28.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 262: blk.29.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 263: blk.29.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 264: blk.29.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 265: blk.29.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 266: blk.29.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 267: blk.29.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 268: blk.29.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 269: blk.29.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 270: blk.29.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 271: blk.30.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 272: blk.30.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 273: blk.30.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 274: blk.30.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 275: blk.30.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 276: blk.30.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 277: blk.30.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 278: blk.30.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 279: blk.30.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 280: blk.31.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 281: blk.31.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 282: blk.31.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 283: blk.31.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 284: blk.31.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 285: blk.31.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 286: blk.31.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 287: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 288: blk.31.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 289: output_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 290: output.weight q8_0 [ 4096, 36608, 1, 1 ]
llama_model_loader: - kv 0: general.architecture str
llama_model_loader: - kv 1: general.name str
llama_model_loader: - kv 2: llama.context_length u32
llama_model_loader: - kv 3: llama.embedding_length u32
llama_model_loader: - kv 4: llama.block_count u32
llama_model_loader: - kv 5: llama.feed_forward_length u32
llama_model_loader: - kv 6: llama.rope.dimension_count u32
llama_model_loader: - kv 7: llama.attention.head_count u32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32
llama_model_loader: - kv 10: llama.rope.freq_base f32
llama_model_loader: - kv 11: general.file_type u32
llama_model_loader: - kv 12: tokenizer.ggml.model str
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr
llama_model_loader: - kv 14: tokenizer.ggml.scores arr
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32
llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32
llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32
llama_model_loader: - kv 19: general.quantization_version u32
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q8_0: 226 tensors
llm_load_vocab: mismatch in special tokens definition ( 361/36608 vs 259/36608 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 36608
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = mostly Q8_0
llm_load_print_meta: model params = 7.28 B
llm_load_print_meta: model size = 7.20 GiB (8.50 BPW)
llm_load_print_meta: general.name = openbuddy_openbuddy-mistral-7b-v13.1
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.11 MB
llm_load_tensors: using ROCm for GPU acceleration
llm_load_tensors: mem required = 152.04 MB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 35/35 layers to GPU
llm_load_tensors: VRAM used: 7224.95 MB
.................................................................................................
llama_new_context_with_model: n_ctx = 512
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: offloading v cache to GPU
llama_kv_cache_init: offloading k cache to GPU
llama_kv_cache_init: VRAM kv self = 64.00 MB
llama_new_context_with_model: kv self size = 64.00 MB
llama_build_graph: non-view tensors processed: 740/740
llama_new_context_with_model: compute buffer total size = 86.13 MB
llama_new_context_with_model: VRAM scratch buffer: 79.50 MB
llama_new_context_with_model: total VRAM used: 7368.45 MB (model: 7224.95 MB, context: 143.50 MB)
CUDA error 98 at /home/super/apps/llama.cpp/ggml-cuda.cu:6779: invalid device function
current device: 0
super@super-System-Product-Name:~/apps/llama.cpp$
I had the same kind of problems too. You have to build it with make, cmake caused the cuda errors for me. And for the stuck loading, try launching it with -no-mmap, you will need enough ram or swap for the full model. I had these problems with 2X MI25
Thanks @8XXD8 Now loading is OK but got endless '#' as output:
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 39564.34 MB
llm_load_tensors: using ROCm for GPU acceleration
ggml_cuda_set_main_device: using device 0 (Radeon RX 7900 XTX) as main device
llm_load_tensors: mem required = 165.64 MB
llm_load_tensors: offloading 80 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 83/83 layers to GPU
llm_load_tensors: VRAM used: 39398.70 MB
....................................................................................................
llama_new_context_with_model: n_ctx = 512
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: offloading v cache to GPU
llama_kv_cache_init: offloading k cache to GPU
llama_kv_cache_init: VRAM kv self = 160.00 MB
llama_new_context_with_model: kv self size = 160.00 MB
llama_build_graph: non-view tensors processed: 1844/1844
llama_new_context_with_model: compute buffer total size = 151.63 MB
llama_new_context_with_model: VRAM scratch buffer: 145.00 MB
llama_new_context_with_model: total VRAM used: 39703.71 MB (model: 39398.70 MB, context: 305.00 MB)
system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 |
sampling:
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
generate: n_ctx = 512, n_batch = 512, n_predict = 1000, n_keep = 0
tell me a very long story################################################################################################################################################################################################################################.......
running:
export HIP_VISIBLE_DEVICES="0,1"
./main -m ../text-generation-webui/models/openbuddy-llama2-70b-v10.1.Q4_K_M.gguf -ngl 100 -p "tell me a very long story" -n 1000 --no-mmap
Have you tried other models? Openbuddy might be sensitive to prompt format, I downloaded the 7b version to test and with your prompt sometimes it gave me only an end of text:
system_info: n_threads = 8 / 16 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 |
sampling:
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
generate: n_ctx = 512, n_batch = 512, n_predict = 1000, n_keep = 0
tell me a very long story
[end of text]
I had some proper responses too.
must i make with
make clean && LLAMA_HIPLAS=1 && HIP_VISIBLE_DEVICES=1 make -j
OR
make clean && LLAMA_HIPLAS=1 && HIP_VISIBLE_DEVICES=0,1 make -j
for multi gpu ?
must i make with
make clean && LLAMA_HIPLAS=1 && HIP_VISIBLE_DEVICES=1 make -j
OR
make clean && LLAMA_HIPLAS=1 && HIP_VISIBLE_DEVICES=0,1 make -j
for multi gpu ?
wow you are so COOL! I got my first proper output by 2 gpu:
export HIP_VISIBLE_DEVICES="0,1"
./main -m ../text-generation-webui/models/samantha-1.1-llama-33b.Q6_K.gguf -ngl 100 -p "tell me a very long story" -n 1000 --no-mmap
got output:
......
llm_load_print_meta: model ftype = mostly Q6_K
llm_load_print_meta: model params = 32.53 B
llm_load_print_meta: model size = 24.85 GiB (6.56 BPW)
llm_load_print_meta: general.name = samantha-1.1-llama-33b.ggmlv3.q6_K.bin
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 25450.39 MB
llm_load_tensors: mem required = 25450.39 MB
....................................................................................................
llama_new_context_with_model: n_ctx = 512
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: kv self size = 780.00 MB
llama_build_graph: non-view tensors processed: 1384/1384
llama_new_context_with_model: compute buffer total size = 103.63 MB
system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 |
sampling:
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
generate: n_ctx = 512, n_batch = 512, n_predict = 1000, n_keep = 0
tell me a very long story, one that can keep us both entertained and engaged while we continue to chat. It's important for me to build our connection through stimulating conversations." [end of text]
congrats so happy for u .. im still at
llm_load_tensors: VRAM used: 36930.11 MB
...........................................................................
btw for make .. what are the commands did you run @wizd ?
btw for make .. what are the commands did you run @wizd ?
you are right. must do gpu selection on compile time.
so its make clean && LLAMA_HIPLAS=1 && HIP_VISIBLE_DEVICES=0,1 make -j
or
make clean && LLAMA_HIPLAS=1 && HIP_VISIBLE_DEVICES="0,1" make -j
if i use
make clean && LLAMA_HIPLAS=1 && HIP_VISIBLE_DEVICES="0,1" make -j
i get
warning: see main README.md for information on enabling GPU BLAS support
warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.
only possible with this make command
make clean && LLAMA_HIPBLAS=1 make -j
and the output is
main: build = 1501 (a75fa57)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed = 1699588357
ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 2 ROCm devices:
Device 0: Radeon RX 7900 XTX, compute capability 11.0
Device 1: Radeon RX 7900 XT, compute capability 11.0
but i get jibberish
Write a function in TypeScript that sums numbers Geography#####################################################
sorry my post was wrong because this line compile without gpu support and runs on cpu:
make clean && LLAMA_HIPLAS=1 && HIP_VISIBLE_DEVICES=0,1 make -j
so the bug is still there. when turn on two gpu:
....................................................................................................
llama_new_context_with_model: n_ctx = 512
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: offloading v cache to GPU
llama_kv_cache_init: offloading k cache to GPU
llama_kv_cache_init: VRAM kv self = 780.00 MB
llama_new_context_with_model: kv self size = 780.00 MB
llama_build_graph: non-view tensors processed: 1384/1384
llama_new_context_with_model: compute buffer total size = 103.63 MB
llama_new_context_with_model: VRAM scratch buffer: 97.00 MB
llama_new_context_with_model: total VRAM used: 26160.57 MB (model: 25283.57 MB, context: 877.00 MB)
system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 |
sampling:
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
generate: n_ctx = 512, n_batch = 512, n_predict = 1000, n_keep = 0
tell me a very long storyviews RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA
yes when i use 2 gpu its a problem
i posted upstream on RCCL github https://github.com/ROCmSoftwarePlatform/rccl/issues/957
sorry my post was wrong because this line compile without gpu support and runs on cpu:
make clean && LLAMA_HIPLAS=1 && HIP_VISIBLE_DEVICES=0,1 make -j
so the bug is still there. when turn on two gpu:
.................................................................................................... llama_new_context_with_model: n_ctx = 512 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: offloading v cache to GPU llama_kv_cache_init: offloading k cache to GPU llama_kv_cache_init: VRAM kv self = 780.00 MB llama_new_context_with_model: kv self size = 780.00 MB llama_build_graph: non-view tensors processed: 1384/1384 llama_new_context_with_model: compute buffer total size = 103.63 MB llama_new_context_with_model: VRAM scratch buffer: 97.00 MB llama_new_context_with_model: total VRAM used: 26160.57 MB (model: 25283.57 MB, context: 877.00 MB) system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | sampling: repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000 top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800 mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000 generate: n_ctx = 512, n_batch = 512, n_predict = 1000, n_keep = 0 tell me a very long storyviews RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA
so its just this that works with gpu
make clean && LLAMA_HIPBLAS=1 make -j
right ?
sorry my post was wrong because this line compile without gpu support and runs on cpu:
make clean && LLAMA_HIPLAS=1 && HIP_VISIBLE_DEVICES=0,1 make -j
so the bug is still there. when turn on two gpu:
.................................................................................................... llama_new_context_with_model: n_ctx = 512 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: offloading v cache to GPU llama_kv_cache_init: offloading k cache to GPU llama_kv_cache_init: VRAM kv self = 780.00 MB llama_new_context_with_model: kv self size = 780.00 MB llama_build_graph: non-view tensors processed: 1384/1384 llama_new_context_with_model: compute buffer total size = 103.63 MB llama_new_context_with_model: VRAM scratch buffer: 97.00 MB llama_new_context_with_model: total VRAM used: 26160.57 MB (model: 25283.57 MB, context: 877.00 MB) system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | sampling: repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000 top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800 mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000 generate: n_ctx = 512, n_batch = 512, n_predict = 1000, n_keep = 0 tell me a very long storyviews RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA RA
so its just this that works with gpu
make clean && LLAMA_HIPBLAS=1 make -j
right ?
No, compiling it like this will only produce output that is compatible only with CPUs.
I dont use HIP_VISIBLE_DEVICES, just
make clean
make LLAMA_HIPBLAS=1
and it works for me. And i run ./main without HIP_VISIBLE_DEVICES, and it uses both cards. Im on Rocm 5.7 and Ubuntu server 22.04.3
I dont use HIP_VISIBLE_DEVICES, just
make clean make LLAMA_HIPBLAS=1
and it works for me. And i run ./main without HIP_VISIBLE_DEVICES, and it uses both cards. Im on Rocm 5.7 and Ubuntu server 22.04.3
Thanks its compiled as per gpu now
Log start
main: build = 1501 (a75fa57)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed = 1699596835
ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 2 ROCm devices:
Device 0: Radeon RX 7900 XTX, compute capability 11.0
Device 1: Radeon RX 7900 XT, compute capability 11.0
but ...
Write in python object class star wars and its charactersDesc##################################################################
still getting jibberish
I dont use HIP_VISIBLE_DEVICES, just
make clean make LLAMA_HIPBLAS=1
and it works for me. And i run ./main without HIP_VISIBLE_DEVICES, and it uses both cards. Im on Rocm 5.7 and Ubuntu server 22.04.3
can you perhaps outline like this guide , from start to finish ... https://www.reddit.com/r/LocalLLaMA/comments/170tghx/guide_installing_rocmhip_for_llamacpp_on_linux/
apparently nvidia also has this problem https://github.com/ggerganov/llama.cpp/issues/3772
HIP_VISIBLE_DEVICES is an environment variable read by ROCm during run time and has not reason to be part of the command line for compiling. Simply running "make LLAMA_HIPBLAS=1" is all that is required on my Ubuntu 22.04 server.
Unclear if this is related, but I can't load any model at all on multigpu ROCm.
Segmentation fault after model load for ROCm multi-gpu, multi-gfx. Best I can remember it worked a couple months ago, but has now been broken at least 2 weeks.
Tested on: Arch Linux Kernel 6.5.9, ROCm 5.7.1, llamacpp https://github.com/ggerganov/llama.cpp/commit/4a4fd3eefad5bd17ab6bcd8e2181b4f62eae76cf
Context
Once it loads it stuck at loading VRAM
My computer is running Dual AMD GPU 7900 XTX and 7900 XT Ubuntu 22.04 , ROCm 5.7
ROCM-SMI Output
$ python3 --version Python 3.10.12 $ make --version GNU Make 4.3 Built for x86_64-pc-linux-gnu $ g++ --version g++ (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Steps to Reproduce
Failure Logs