ShareGPT4V-7B-- a multimodel model that surpasses Llava

[itsPreto]

are there plans to support this new SOTA open source vision model?

--despite its compact size, the model is able to extract text from images with incredible accuracy.

[BarfingLemurs]

ShareGPT4V: #4172

[cmp-nct]

I'm using it since a while, it's great
You can just copy the new conversion script from my linked PR and use that instead of the old one, example usage is in the PR.

[itsPreto]

Thats great news, indeed! So I downloaded the files required, threw them in a folder named ShareGPT4V-7B_Pretrained_vit-large336-l12 but not sure where this llava.projector comes from. Do you mind elaborating a bit?

@cmp-nct

[cmp-nct]

@itsPreto Don't smash all of it together, each folder has an own config.json.
You need the models and the vision encodes, both from ShareGPT4 in two folders.
So the model and the vision tower (https://huggingface.co/Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12)
Then overwrite the conversion script in examples/llava/ with my PR.

Everything else is as described here:
https://github.com/ggerganov/llama.cpp/blob/master/examples/llava/README.md
With the exception that you add --clip_model_is_vision to the image encoder python script

[itsPreto]

main:    total time =  7891.93 ms
❯ ./llava-cli -m models/shareGPT4V-7B/shareGPT4V-7B-q4_0.gguf --mmproj models/shareGPT4V-7B/mmproj-model-f16.gguf --image ../ippo.png
gguf_init_from_file: GGUFv1 is no longer supported. please use a more up-to-date version
libc++abi: terminating due to uncaught exception of type std::runtime_error: clip_model_load: failed to load CLIP model from models/shareGPT4V-7B/mmproj-model-f16.gguf. Does this file exist?

[1]    12232 abort      ./llava-cli -m models/shareGPT4V-7B/shareGPT4V-7B-q4_0.gguf --mmproj  --image

@cmp-nct Ty! I was able to convert/split/quantize the model but I'm not able to actually run it due to an old gguf format? Not sure why that would be since I have the latest from master branch and replaced the simply replace the relevant parts of convert-image-encoder-to-gguf.py with your changes.

Any ideas?

Here's the directory for the model:

models/shareGPT4V-7B
├── config.json
├── generation_config.json
├── ggml-model-f16.gguf
├── llava.projector
├── mmproj-model-f16.gguf
├── pytorch_model-00001-of-00002.bin
├── pytorch_model-00002-of-00002.bin
├── pytorch_model.bin.index.json
├── shareGPT4V-7B-q4_0.gguf
├── special_tokens_map.json
├── tokenizer.model
├── tokenizer_config.json
└── tower
    ├── config.json
    ├── preprocessor_config.json
    └── pytorch_model.bin

[cmp-nct]

It's not related to the image converter.
You sure your bin/Release/quantize is up to date and not very old ?
No matter what gguf version was produced with python, the quantize would update it to latest.
Can you verify the filesize of the gguf too ? If it's plausible size.

Also, I'd use q4_k not q4_0, if you are on a recent release you can use K quants on llava (about 40 tensors will fallback to compatibility quants)

For your reference:

[itsPreto]

@cmp-nct

./models/shareGPT4V-7B/shareGPT4V-7B-q4_k.gguf (3.80GB).

Just to make sure I'm not crazy I cloned the project fresh, ran through the instructions again and quantized to q4_k instead as you suggested-- I'm still getting the same error? Is there anything in particular that I should be doing other than running make then make llava-cli?

I've been quantizing my LLMs up until now just fine (text models), have yet to run into this issue with quantize.

[cmp-nct]

.\build\bin\Release\llava-cli.exe -m Q:\models\llava\ShareGPT4V-7B\ggml-model-q6_k --mmproj Q:\models\llava\ShareGPT4V-7B\mmproj-model-f16.gguf  -ngl 80 -b 1024 --image c:\temp\tmp.png
ggml_init_cublas: GGML_CUDA_FORCE_MMQ:   no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 2 CUDA devices:
  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9
  Device 1: NVIDIA GeForce RTX 3090, compute capability 8.6
clip_model_load: model name:   ShareGPT4V-7B_Pretrained_vit-large336-l12
clip_model_load: description:  image encoder for LLaVA
clip_model_load: GGUF version: 2
clip_model_load: alignment:    32
clip_model_load: n_tensors:    377
clip_model_load: n_kv:         18
clip_model_load: ftype:        f16

That is how it should look like, it looks like something is wrong with the projector, make sure your files are all complete.
I'd restart fresh over, removing all new files.
llava-surgery.py -> convert-image-encoder-to-gguf.py -> convert.py -> quantize

py convert-image-encoder-to-gguf.py -m ShareGPT4V-7B_Pretrained_vit-large336-l12/ --llava-projector ShareGPT4V-7B/llava.projector --output-dir ShareGPT4V-7B --clip_model_is_vision

I've no idea where your problem originates from, so far best I can recommend is to ensure your python files are complete and then separate them into two directories (just as they come from HF git), so you don't deviate from the usual process and possibly hit something untested.

[itsPreto]

Okay I'm strictly following the steps in the example:

❯ python ./examples/llava/llava-surgery.py -m models/shareGPT4V-7B
Done!
Now you can convert models/shareGPT4V-7B to a a regular LLaMA GGUF file.
Also, use models/shareGPT4V-7B/llava.projector to prepare a llava-encoder.gguf file.
❯ cd models/shareGPT4V-7B
❯ ls
README.md                        pytorch_model-00002-of-00002.bin
config.json                      pytorch_model.bin.index.json
generation_config.json           special_tokens_map.json
llava.projector                  tokenizer.model
pytorch_model-00001-of-00002.bin tokenizer_config.json

llava.projector successfully generated so I'll run convert-image-encoder-to-gguf.py:

❯ python ./examples/llava/convert-image-encoder-to-gguf.py -m  models/ShareGPT4V-7B_Pretrained_vit-large336-l12/ --llava-projector  models/shareGPT4V-7B/llava.projector 
--output-dir models/shareGPT4V-7B --clip_model_is_vision


gguf: This GGUF file is for Little Endian only
Projector tensors added

Converting to float32
v.class_embd - f32 - shape = (1024,)
tensor v.patch_embd.weight is always saved in f16
v.patch_embd.weight - f16 - shape = (1024, 3, 14, 14)
Converting to float16
v.position_embd.weight - f16 - shape = (577, 1024)
.....
.....
.....
v.blk.22.ln2.bias - f32 - shape = (1024,)
skipping parameter: vision_model.post_layernorm.weight
Done. Output file: models/shareGPT4V-7B/mmproj-model-f16.gguf

double checking files in shareGPT4V-7B:

❯ cd models/shareGPT4V-7B
❯ ls
README.md                        pytorch_model-00002-of-00002.bin
config.json                      pytorch_model.bin.index.json
generation_config.json           special_tokens_map.json
llava.projector                  tokenizer.model
mmproj-model-f16.gguf            tokenizer_config.json
pytorch_model-00001-of-00002.bin

running convert.py on the pytorch .bin model parts:

❯ python convert.py models/shareGPT4V-7B
Loading model file models/shareGPT4V-7B/pytorch_model-00001-of-00002.bin
Loading model file models/shareGPT4V-7B/pytorch_model-00001-of-00002.bin
Loading model file models/shareGPT4V-7B/pytorch_model-00002-of-00002.bin
params = Params(n_vocab=32000, n_embd=4096, n_layer=32, n_ctx=4096, n_ff=11008, n_head=32, n_head_kv=32, f_norm_eps=1e-05, rope_scaling_type=None, f_rope_freq_base=None, f_rope_scale=None, n_orig_ctx=None, rope_finetuned=None, ftype=None, path_model=PosixPath('models/shareGPT4V-7B'))
Loading vocab file 'models/shareGPT4V-7B/tokenizer.model', type 'spm'
Permuting layer 0
Permuting layer 1
Permuting layer 2
Permuting layer 3
Permuting layer 4
Permuting layer 5

.....
.....
.....

model.layers.31.post_attention_layernorm.weight  -> blk.31.ffn_norm.weight                   | BF16   | [4096]
model.norm.weight                                -> output_norm.weight                       | BF16   | [4096]
lm_head.weight                                   -> output.weight                            | BF16   | [32000, 4096]
Writing models/shareGPT4V-7B/ggml-model-f16.gguf, format 1
gguf: This GGUF file is for Little Endian only
gguf: Setting special token type bos to 1
gguf: Setting special token type eos to 2
gguf: Setting special token type pad to 0
gguf: Setting add_bos_token to True
gguf: Setting add_eos_token to False
[  1/291] Writing tensor token_embd.weight                      | size  32000 x   4096  | type F16  | T+   0
[  2/291] Writing tensor blk.0.attn_q.weight                    | size   4096 x   4096  | type F16  | T+   0
[  3/291] Writing tensor blk.0.attn_k.weight                    | size   4096 x   4096  | type F16  | T+   0

  4096  | type F16  | T+   8
Wrote models/shareGPT4V-7B/ggml-model-f16.gguf

this generated the gguf variant in f16 (not sure if this was supposed to be f32):

❯ cd models/shareGPT4V-7B
❯ ls
README.md                        pytorch_model-00001-of-00002.bin
config.json                      pytorch_model-00002-of-00002.bin
generation_config.json           pytorch_model.bin.index.json
ggml-model-f16.gguf              special_tokens_map.json
llava.projector                  tokenizer.model
mmproj-model-f16.gguf            tokenizer_config.json

all the files seem to be there, lets try quantizing the ggml-model-f16.gguf again:

❯ ./quantize models/shareGPT4V-7B/ggml-model-f16.gguf models/shareGPT4V-7B/shareGPT4V-7B-q4_K_M.gguf Q4_K_M
main: build = 1560 (e9c13ff)
main: built with Apple clang version 15.0.0 (clang-1500.0.40.1) for arm64-apple-darwin23.1.0
main: quantizing 'models/shareGPT4V-7B/ggml-model-f16.gguf' to 'models/shareGPT4V-7B/shareGPT4V-7B-q4_K_M.gguf' as Q4_K_M
llama_model_loader: loaded meta data with 20 key-value pairs and 291 tensors from models/shareGPT4V-7B/ggml-model-f16.gguf (version GGUF V3 (latest))
llama_model_loader: - tensor    0:                token_embd.weight f16      [  4096, 32000,     1,     1 ]
llama_model_loader: - tensor    1:              blk.0.attn_q.weight f16      [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor    2:              blk.0.attn_k.weight f16      [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor    3:              blk.0.attn_v.weight f16      [  4096,  4096,     1,     1 ]

notice this: version GGUF V3 (latest)

llama_model_loader: - tensor  290:                    output.weight f16      [  4096, 32000,     1,     1 ]
llama_model_loader: - kv   0:                       general.architecture str              = llama
llama_model_loader: - kv   1:                               general.name str              = LLaMA v2
llama_model_loader: - kv   2:                       llama.context_length u32              = 4096
llama_model_loader: - kv   3:                     llama.embedding_length u32              = 4096
llama_model_loader: - kv   4:                          llama.block_count u32              = 32
llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 11008
llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 32
llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  10:                          general.file_type u32              = 1
llama_model_loader: - kv  11:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  12:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv  13:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv  15:                tokenizer.ggml.bos_token_id u32              = 1
llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32              = 2
llama_model_loader: - kv  17:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  18:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  19:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - type  f32:   65 tensors
llama_model_loader: - type  f16:  226 tensors
llama_model_quantize_internal: meta size = 741152 bytes
[   1/ 291]

.....
.....
.....

main: quantize time = 59258.60 ms
main:    total time = 59258.60 ms

checking files once again:

❯ cd models/shareGPT4V-7B
❯ ls
README.md                        ggml-model-f16.gguf              pytorch_model-00001-of-00002.bin shareGPT4V-7B-q4_K_M.gguf             tokenizer_config.json
config.json                      llava.projector                  pytorch_model-00002-of-00002.bin special_tokens_map.json
generation_config.json           mmproj-model-f16.gguf            pytorch_model.bin.index.json     tokenizer.model

lets try running it now:

> make llava-cli

make: `llava-cli' is up to date.


❯ ./llava-cli -m ./models/shareGPT4V-7B/shareGPT4V-7B-q4_K_M.gguf --mmproj ./models/shareGPT4V-7B/mmproj-model-f16.gguf --image ../ippo.png
clip_model_load: model name:   ShareGPT4V-7B_Pretrained_vit-large336-l12
clip_model_load: description:  image encoder for LLaVA
clip_model_load: GGUF version: 3
clip_model_load: alignment:    32
clip_model_load: n_tensors:    377
clip_model_load: n_kv:         18
clip_model_load: ftype:        f16

clip_model_load: text_encoder:   0
clip_model_load: vision_encoder: 1
clip_model_load: llava_projector:  1
clip_model_load: model size:     595.62 MB
clip_model_load: metadata size:  0.14 MB
clip_model_load: total allocated memory: 195.95 MB
llama_model_loader: loaded meta data with 21 key-value pairs and 291 tensors from ./models/shareGPT4V-7B/shareGPT4V-7B-q4_K_M.gguf (version GGUF V3 (latest))

.....
.....
.....


llm_load_vocab: special tokens definition check successful ( 259/32000 ).
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          = 32000
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: n_ctx_train      = 4096
llm_load_print_meta: n_embd           = 4096
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 32
llm_load_print_meta: n_layer          = 32
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_gqa            = 1
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             = 11008
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  = 4096
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: model type       = 7B
llm_load_print_meta: model ftype      = mostly Q4_K - Medium
llm_load_print_meta: model params     = 6.74 B
llm_load_print_meta: model size       = 3.80 GiB (4.84 BPW)
llm_load_print_meta: general.name   = LLaMA v2
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: PAD token = 0 '<unk>'
llm_load_print_meta: LF token  = 13 '<0x0A>'
llm_load_tensors: ggml ctx size =    0.11 MiB
llm_load_tensors: mem required  = 3891.35 MiB
..................................................................................................
llama_new_context_with_model: n_ctx      = 2048
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  = 1024.00 MiB
llama_build_graph: non-view tensors processed: 740/740
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1 Pro
ggml_metal_init: picking default device: Apple M1 Pro
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: loading '/Users/marconeves/Library/CloudStorage/OneDrive-DeltaAirLines/Desktop/desktop/projects/llama.cpp/ggml-metal.metal'
ggml_metal_init: GPU name:   Apple M1 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple7 (1007)
ggml_metal_init: hasUnifiedMemory              = true
ggml_metal_init: recommendedMaxWorkingSetSize  = 10922.67 MiB
ggml_metal_init: maxTransferRate               = built-in GPU
llama_new_context_with_model: compute buffer total size = 159.07 MiB
llama_new_context_with_model: max tensor size =   102.54 MiB
ggml_metal_add_buffer: allocated 'data            ' buffer, size =  3891.95 MiB, ( 3892.58 / 10922.67)
ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1024.02 MiB, ( 4916.59 / 10922.67)
ggml_metal_add_buffer: allocated 'alloc           ' buffer, size =   156.02 MiB, ( 5072.61 / 10922.67)

encode_image_with_clip: image encoded in  1258.81 ms by CLIP (    2.19 ms per image patch)

 The image portrays a dynamic scene from a comic book. The central figure is a young man with dark hair, dressed in a t-shirt and pants. He is captured mid-action, his body leaning forward with a determined expression on his face, suggesting he is engaged in a high-stakes battle.

In his right hand, he wields a boxing glove, indicating that he is a boxer. His left hand is raised, possibly in the midst of a powerful punch. The background of the image is ablaze, with bright flashes of light illuminating the scene, adding to the intensity of the moment.

The entire image is rendered in black and white, with the exception of the boxer's glove and the bright background, which are colored. The use of grayscale in the image gives it a dramatic and intense feel, enhancing the visual impact of the scene.

Please note that this description is based on the visible elements in the image and does not include any inferred or imagined details.

llama_print_timings:        load time =    6712.99 ms
llama_print_timings:      sample time =       5.57 ms /   230 runs   (    0.02 ms per token, 41314.89 tokens per second)
llama_print_timings: prompt eval time =    7968.92 ms /   617 tokens (   12.92 ms per token,    77.43 tokens per second)
llama_print_timings:        eval time =    8815.57 ms /   230 runs   (   38.33 ms per token,    26.09 tokens per second)
llama_print_timings:       total time =   18693.08 ms
ggml_metal_free: deallocating

Wow! I have no clue why it worked this time around since I did virtually nothing different... mildly infuriating but it finally works and works GREAT! Thanks! @cmp-nct

[winer632]

I followed the steps @itsPreto listed in the above comment. However it popped up an error message in the last step.
How did I get this error? Could anyone help? @cmp-nct @itsPreto

[winer632]

I changed verbosity of this line to 3 in llava-cli.cpp, remake llava-cli and got the detailed info.

(base) [root@k8s-0 llama.cpp]# ./llava-cli -m ./models/ShareGPT4V-7B/ShareGPT4V-7B-q4_K_M.gguf --mmproj ./models/ShareGPT4V-7B/mmproj-model-f16.gguf --image ../test_photo/zhuanma.jpeg
clip_model_load: model name:   ShareGPT4V-7B_Pretrained_vit-large336-l12
clip_model_load: description:  image encoder for LLaVA
clip_model_load: GGUF version: 3
clip_model_load: alignment:    32
clip_model_load: n_tensors:    373
clip_model_load: n_kv:         18
clip_model_load: ftype:        f16

clip_model_load: kv[0]: key = general.architecture
clip_model_load: kv[1]: key = clip.has_text_encoder
clip_model_load: kv[2]: key = clip.has_vision_encoder
clip_model_load: kv[3]: key = clip.has_llava_projector
clip_model_load: kv[4]: key = general.file_type
clip_model_load: kv[5]: key = general.name
clip_model_load: kv[6]: key = general.description
clip_model_load: kv[7]: key = clip.vision.image_size
clip_model_load: kv[8]: key = clip.vision.patch_size
clip_model_load: kv[9]: key = clip.vision.embedding_length
clip_model_load: kv[10]: key = clip.vision.feed_forward_length
clip_model_load: kv[11]: key = clip.vision.projection_dim
clip_model_load: kv[12]: key = clip.vision.attention.head_count
clip_model_load: kv[13]: key = clip.vision.attention.layer_norm_epsilon
clip_model_load: kv[14]: key = clip.vision.block_count
clip_model_load: kv[15]: key = clip.vision.image_mean
clip_model_load: kv[16]: key = clip.vision.image_std
clip_model_load: kv[17]: key = clip.use_gelu

clip_model_load: tensor[0]: n_dims = 1, name = v.class_embd, tensor_size=4096, padded_size=4096, offset=0
clip_model_load: tensor[1]: n_dims = 4, name = v.patch_embd.weight, tensor_size=1204224, padded_size=1204224, offset=4096
clip_model_load: tensor[2]: n_dims = 2, name = v.position_embd.weight, tensor_size=1181696, padded_size=1181696, offset=1208320
clip_model_load: tensor[3]: n_dims = 1, name = v.pre_ln.weight, tensor_size=4096, padded_size=4096, offset=2390016
clip_model_load: tensor[4]: n_dims = 1, name = v.pre_ln.bias, tensor_size=4096, padded_size=4096, offset=2394112
clip_model_load: tensor[5]: n_dims = 2, name = v.blk.0.attn_k.weight, tensor_size=2097152, padded_size=2097152, offset=2398208
clip_model_load: tensor[6]: n_dims = 1, name = v.blk.0.attn_k.bias, tensor_size=4096, padded_size=4096, offset=4495360
clip_model_load: tensor[7]: n_dims = 2, name = v.blk.0.attn_v.weight, tensor_size=2097152, padded_size=2097152, offset=4499456
clip_model_load: tensor[8]: n_dims = 1, name = v.blk.0.attn_v.bias, tensor_size=4096, padded_size=4096, offset=6596608
clip_model_load: tensor[9]: n_dims = 2, name = v.blk.0.attn_q.weight, tensor_size=2097152, padded_size=2097152, offset=6600704
clip_model_load: tensor[10]: n_dims = 1, name = v.blk.0.attn_q.bias, tensor_size=4096, padded_size=4096, offset=8697856
clip_model_load: tensor[11]: n_dims = 2, name = v.blk.0.attn_out.weight, tensor_size=2097152, padded_size=2097152, offset=8701952
clip_model_load: tensor[12]: n_dims = 1, name = v.blk.0.attn_out.bias, tensor_size=4096, padded_size=4096, offset=10799104
clip_model_load: tensor[13]: n_dims = 1, name = v.blk.0.ln1.weight, tensor_size=4096, padded_size=4096, offset=10803200
clip_model_load: tensor[14]: n_dims = 1, name = v.blk.0.ln1.bias, tensor_size=4096, padded_size=4096, offset=10807296
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clip_model_load: tensor[306]: n_dims = 1, name = v.blk.18.ffn_up.bias, tensor_size=4096, padded_size=4096, offset=481548288
clip_model_load: tensor[307]: n_dims = 1, name = v.blk.18.ln2.weight, tensor_size=4096, padded_size=4096, offset=481552384
clip_model_load: tensor[308]: n_dims = 1, name = v.blk.18.ln2.bias, tensor_size=4096, padded_size=4096, offset=481556480
clip_model_load: tensor[309]: n_dims = 2, name = v.blk.19.attn_k.weight, tensor_size=2097152, padded_size=2097152, offset=481560576
clip_model_load: tensor[310]: n_dims = 1, name = v.blk.19.attn_k.bias, tensor_size=4096, padded_size=4096, offset=483657728
clip_model_load: tensor[311]: n_dims = 2, name = v.blk.19.attn_v.weight, tensor_size=2097152, padded_size=2097152, offset=483661824
clip_model_load: tensor[312]: n_dims = 1, name = v.blk.19.attn_v.bias, tensor_size=4096, padded_size=4096, offset=485758976
clip_model_load: tensor[313]: n_dims = 2, name = v.blk.19.attn_q.weight, tensor_size=2097152, padded_size=2097152, offset=485763072
clip_model_load: tensor[314]: n_dims = 1, name = v.blk.19.attn_q.bias, tensor_size=4096, padded_size=4096, offset=487860224
clip_model_load: tensor[315]: n_dims = 2, name = v.blk.19.attn_out.weight, tensor_size=2097152, padded_size=2097152, offset=487864320
clip_model_load: tensor[316]: n_dims = 1, name = v.blk.19.attn_out.bias, tensor_size=4096, padded_size=4096, offset=489961472
clip_model_load: tensor[317]: n_dims = 1, name = v.blk.19.ln1.weight, tensor_size=4096, padded_size=4096, offset=489965568
clip_model_load: tensor[318]: n_dims = 1, name = v.blk.19.ln1.bias, tensor_size=4096, padded_size=4096, offset=489969664
clip_model_load: tensor[319]: n_dims = 2, name = v.blk.19.ffn_down.weight, tensor_size=8388608, padded_size=8388608, offset=489973760
clip_model_load: tensor[320]: n_dims = 1, name = v.blk.19.ffn_down.bias, tensor_size=16384, padded_size=16384, offset=498362368
clip_model_load: tensor[321]: n_dims = 2, name = v.blk.19.ffn_up.weight, tensor_size=8388608, padded_size=8388608, offset=498378752
clip_model_load: tensor[322]: n_dims = 1, name = v.blk.19.ffn_up.bias, tensor_size=4096, padded_size=4096, offset=506767360
clip_model_load: tensor[323]: n_dims = 1, name = v.blk.19.ln2.weight, tensor_size=4096, padded_size=4096, offset=506771456
clip_model_load: tensor[324]: n_dims = 1, name = v.blk.19.ln2.bias, tensor_size=4096, padded_size=4096, offset=506775552
clip_model_load: tensor[325]: n_dims = 2, name = v.blk.20.attn_k.weight, tensor_size=2097152, padded_size=2097152, offset=506779648
clip_model_load: tensor[326]: n_dims = 1, name = v.blk.20.attn_k.bias, tensor_size=4096, padded_size=4096, offset=508876800
clip_model_load: tensor[327]: n_dims = 2, name = v.blk.20.attn_v.weight, tensor_size=2097152, padded_size=2097152, offset=508880896
clip_model_load: tensor[328]: n_dims = 1, name = v.blk.20.attn_v.bias, tensor_size=4096, padded_size=4096, offset=510978048
clip_model_load: tensor[329]: n_dims = 2, name = v.blk.20.attn_q.weight, tensor_size=2097152, padded_size=2097152, offset=510982144
clip_model_load: tensor[330]: n_dims = 1, name = v.blk.20.attn_q.bias, tensor_size=4096, padded_size=4096, offset=513079296
clip_model_load: tensor[331]: n_dims = 2, name = v.blk.20.attn_out.weight, tensor_size=2097152, padded_size=2097152, offset=513083392
clip_model_load: tensor[332]: n_dims = 1, name = v.blk.20.attn_out.bias, tensor_size=4096, padded_size=4096, offset=515180544
clip_model_load: tensor[333]: n_dims = 1, name = v.blk.20.ln1.weight, tensor_size=4096, padded_size=4096, offset=515184640
clip_model_load: tensor[334]: n_dims = 1, name = v.blk.20.ln1.bias, tensor_size=4096, padded_size=4096, offset=515188736
clip_model_load: tensor[335]: n_dims = 2, name = v.blk.20.ffn_down.weight, tensor_size=8388608, padded_size=8388608, offset=515192832
clip_model_load: tensor[336]: n_dims = 1, name = v.blk.20.ffn_down.bias, tensor_size=16384, padded_size=16384, offset=523581440
clip_model_load: tensor[337]: n_dims = 2, name = v.blk.20.ffn_up.weight, tensor_size=8388608, padded_size=8388608, offset=523597824
clip_model_load: tensor[338]: n_dims = 1, name = v.blk.20.ffn_up.bias, tensor_size=4096, padded_size=4096, offset=531986432
clip_model_load: tensor[339]: n_dims = 1, name = v.blk.20.ln2.weight, tensor_size=4096, padded_size=4096, offset=531990528
clip_model_load: tensor[340]: n_dims = 1, name = v.blk.20.ln2.bias, tensor_size=4096, padded_size=4096, offset=531994624
clip_model_load: tensor[341]: n_dims = 2, name = v.blk.21.attn_k.weight, tensor_size=2097152, padded_size=2097152, offset=531998720
clip_model_load: tensor[342]: n_dims = 1, name = v.blk.21.attn_k.bias, tensor_size=4096, padded_size=4096, offset=534095872
clip_model_load: tensor[343]: n_dims = 2, name = v.blk.21.attn_v.weight, tensor_size=2097152, padded_size=2097152, offset=534099968
clip_model_load: tensor[344]: n_dims = 1, name = v.blk.21.attn_v.bias, tensor_size=4096, padded_size=4096, offset=536197120
clip_model_load: tensor[345]: n_dims = 2, name = v.blk.21.attn_q.weight, tensor_size=2097152, padded_size=2097152, offset=536201216
clip_model_load: tensor[346]: n_dims = 1, name = v.blk.21.attn_q.bias, tensor_size=4096, padded_size=4096, offset=538298368
clip_model_load: tensor[347]: n_dims = 2, name = v.blk.21.attn_out.weight, tensor_size=2097152, padded_size=2097152, offset=538302464
clip_model_load: tensor[348]: n_dims = 1, name = v.blk.21.attn_out.bias, tensor_size=4096, padded_size=4096, offset=540399616
clip_model_load: tensor[349]: n_dims = 1, name = v.blk.21.ln1.weight, tensor_size=4096, padded_size=4096, offset=540403712
clip_model_load: tensor[350]: n_dims = 1, name = v.blk.21.ln1.bias, tensor_size=4096, padded_size=4096, offset=540407808
clip_model_load: tensor[351]: n_dims = 2, name = v.blk.21.ffn_down.weight, tensor_size=8388608, padded_size=8388608, offset=540411904
clip_model_load: tensor[352]: n_dims = 1, name = v.blk.21.ffn_down.bias, tensor_size=16384, padded_size=16384, offset=548800512
clip_model_load: tensor[353]: n_dims = 2, name = v.blk.21.ffn_up.weight, tensor_size=8388608, padded_size=8388608, offset=548816896
clip_model_load: tensor[354]: n_dims = 1, name = v.blk.21.ffn_up.bias, tensor_size=4096, padded_size=4096, offset=557205504
clip_model_load: tensor[355]: n_dims = 1, name = v.blk.21.ln2.weight, tensor_size=4096, padded_size=4096, offset=557209600
clip_model_load: tensor[356]: n_dims = 1, name = v.blk.21.ln2.bias, tensor_size=4096, padded_size=4096, offset=557213696
clip_model_load: tensor[357]: n_dims = 2, name = v.blk.22.attn_k.weight, tensor_size=2097152, padded_size=2097152, offset=557217792
clip_model_load: tensor[358]: n_dims = 1, name = v.blk.22.attn_k.bias, tensor_size=4096, padded_size=4096, offset=559314944
clip_model_load: tensor[359]: n_dims = 2, name = v.blk.22.attn_v.weight, tensor_size=2097152, padded_size=2097152, offset=559319040
clip_model_load: tensor[360]: n_dims = 1, name = v.blk.22.attn_v.bias, tensor_size=4096, padded_size=4096, offset=561416192
clip_model_load: tensor[361]: n_dims = 2, name = v.blk.22.attn_q.weight, tensor_size=2097152, padded_size=2097152, offset=561420288
clip_model_load: tensor[362]: n_dims = 1, name = v.blk.22.attn_q.bias, tensor_size=4096, padded_size=4096, offset=563517440
clip_model_load: tensor[363]: n_dims = 2, name = v.blk.22.attn_out.weight, tensor_size=2097152, padded_size=2097152, offset=563521536
clip_model_load: tensor[364]: n_dims = 1, name = v.blk.22.attn_out.bias, tensor_size=4096, padded_size=4096, offset=565618688
clip_model_load: tensor[365]: n_dims = 1, name = v.blk.22.ln1.weight, tensor_size=4096, padded_size=4096, offset=565622784
clip_model_load: tensor[366]: n_dims = 1, name = v.blk.22.ln1.bias, tensor_size=4096, padded_size=4096, offset=565626880
clip_model_load: tensor[367]: n_dims = 2, name = v.blk.22.ffn_down.weight, tensor_size=8388608, padded_size=8388608, offset=565630976
clip_model_load: tensor[368]: n_dims = 1, name = v.blk.22.ffn_down.bias, tensor_size=16384, padded_size=16384, offset=574019584
clip_model_load: tensor[369]: n_dims = 2, name = v.blk.22.ffn_up.weight, tensor_size=8388608, padded_size=8388608, offset=574035968
clip_model_load: tensor[370]: n_dims = 1, name = v.blk.22.ffn_up.bias, tensor_size=4096, padded_size=4096, offset=582424576
clip_model_load: tensor[371]: n_dims = 1, name = v.blk.22.ln2.weight, tensor_size=4096, padded_size=4096, offset=582428672
clip_model_load: tensor[372]: n_dims = 1, name = v.blk.22.ln2.bias, tensor_size=4096, padded_size=4096, offset=582432768
clip_model_load: text_encoder:   0
clip_model_load: vision_encoder: 1
clip_model_load: llava_projector:  1
clip_model_load: model size:     555.59 MB
clip_model_load: metadata size:  0.14 MB

clip_model_load: vision model hparams
image_size         336
patch_size         14
v_hidden_size      1024
v_n_intermediate   4096
v_projection_dim   768
v_n_head           16
v_n_layer          23
terminate called after throwing an instance of 'std::runtime_error'
  what():  get_tensor: unable to find tensor mm.0.weight

已放弃
(base) [root@k8s-0 llama.cpp]#