UPD. New chekpoint. Safetensor
Browse files- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +600 -0
- modeling_phi3_v.py +7 -6
- sample_inference.py +129 -0
- sft_args.json +20 -20
model-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0807f43c073e941583d649eac76fa06b448e5b761824464efb3d1e0558d2f186
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size 4944121600
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model-00002-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d8fb4210c1910dc311987343e81586186d1d0a2e1f403945a6e65adbcb2402a3
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size 3349208696
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model.safetensors.index.json
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|
538 |
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"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.6.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
539 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.6.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
540 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.6.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
541 |
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"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.layer_norm1.bias": "model-00001-of-00002.safetensors",
|
542 |
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"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.layer_norm1.weight": "model-00001-of-00002.safetensors",
|
543 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.layer_norm2.bias": "model-00001-of-00002.safetensors",
|
544 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.layer_norm2.weight": "model-00001-of-00002.safetensors",
|
545 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
546 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
547 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
548 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
549 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
550 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
551 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.self_attn.out_proj.bias": "model-00001-of-00002.safetensors",
|
552 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.self_attn.out_proj.weight": "model-00001-of-00002.safetensors",
|
553 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
554 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
555 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
556 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.7.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
557 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.layer_norm1.bias": "model-00001-of-00002.safetensors",
|
558 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.layer_norm1.weight": "model-00001-of-00002.safetensors",
|
559 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.layer_norm2.bias": "model-00001-of-00002.safetensors",
|
560 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.layer_norm2.weight": "model-00001-of-00002.safetensors",
|
561 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
562 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
563 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
564 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
565 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
566 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
567 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.self_attn.out_proj.bias": "model-00001-of-00002.safetensors",
|
568 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.self_attn.out_proj.weight": "model-00001-of-00002.safetensors",
|
569 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
570 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
571 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
572 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
573 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.layer_norm1.bias": "model-00001-of-00002.safetensors",
|
574 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.layer_norm1.weight": "model-00001-of-00002.safetensors",
|
575 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.layer_norm2.bias": "model-00001-of-00002.safetensors",
|
576 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.layer_norm2.weight": "model-00001-of-00002.safetensors",
|
577 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
578 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
579 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
580 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
581 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
582 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
583 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.self_attn.out_proj.bias": "model-00001-of-00002.safetensors",
|
584 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.self_attn.out_proj.weight": "model-00001-of-00002.safetensors",
|
585 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
586 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
587 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
588 |
+
"model.vision_embed_tokens.img_processor.vision_model.encoder.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
589 |
+
"model.vision_embed_tokens.img_processor.vision_model.post_layernorm.bias": "model-00001-of-00002.safetensors",
|
590 |
+
"model.vision_embed_tokens.img_processor.vision_model.post_layernorm.weight": "model-00001-of-00002.safetensors",
|
591 |
+
"model.vision_embed_tokens.img_processor.vision_model.pre_layrnorm.bias": "model-00001-of-00002.safetensors",
|
592 |
+
"model.vision_embed_tokens.img_processor.vision_model.pre_layrnorm.weight": "model-00001-of-00002.safetensors",
|
593 |
+
"model.vision_embed_tokens.img_projection.0.bias": "model-00001-of-00002.safetensors",
|
594 |
+
"model.vision_embed_tokens.img_projection.0.weight": "model-00001-of-00002.safetensors",
|
595 |
+
"model.vision_embed_tokens.img_projection.2.bias": "model-00001-of-00002.safetensors",
|
596 |
+
"model.vision_embed_tokens.img_projection.2.weight": "model-00001-of-00002.safetensors",
|
597 |
+
"model.vision_embed_tokens.sub_GN": "model-00001-of-00002.safetensors",
|
598 |
+
"model.vision_embed_tokens.wte.weight": "model-00001-of-00002.safetensors"
|
599 |
+
}
|
600 |
+
}
|
modeling_phi3_v.py
CHANGED
@@ -40,7 +40,6 @@ from transformers.utils import (
|
|
40 |
add_code_sample_docstrings,
|
41 |
add_start_docstrings,
|
42 |
add_start_docstrings_to_model_forward,
|
43 |
-
is_flash_attn_2_available,
|
44 |
is_flash_attn_greater_or_equal_2_10,
|
45 |
logging,
|
46 |
replace_return_docstrings,
|
@@ -49,11 +48,13 @@ from .configuration_phi3_v import Phi3VConfig
|
|
49 |
from .image_embedding_phi3_v import Phi3ImageEmbedding
|
50 |
|
51 |
|
52 |
-
|
53 |
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
54 |
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
55 |
|
56 |
_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
|
|
|
|
|
57 |
|
58 |
logger = logging.get_logger(__name__)
|
59 |
|
@@ -1000,8 +1001,8 @@ PHI3V_INPUTS_DOCSTRING = r"""
|
|
1000 |
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
1001 |
model's internal embedding lookup matrix.
|
1002 |
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
|
1003 |
-
The tensors corresponding to the input images. Pixel values can be obtained using [`AutoImageProcessor`].
|
1004 |
-
See [`Phi3ImageProcessor.__call__`] for details.
|
1005 |
image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`, *optional*):
|
1006 |
The sizes of the images in the batch, being (height, width) for each image.
|
1007 |
use_cache (`bool`, *optional*):
|
@@ -1046,7 +1047,7 @@ class Phi3VModel(Phi3VPreTrainedModel):
|
|
1046 |
**config.embd_layer
|
1047 |
}
|
1048 |
self.vision_embed_tokens = Phi3ImageEmbedding(config, wte=self.embed_tokens, **embedding_config)
|
1049 |
-
# # set wte the same for vision embedding
|
1050 |
# self.vision_embed_tokens.wte.weight = self.embed_tokens.weight
|
1051 |
|
1052 |
self.layers = nn.ModuleList(
|
@@ -1629,4 +1630,4 @@ class Phi3VForTokenClassification(Phi3VPreTrainedModel):
|
|
1629 |
logits=logits,
|
1630 |
hidden_states=model_outputs.hidden_states,
|
1631 |
attentions=model_outputs.attentions,
|
1632 |
-
)
|
|
|
40 |
add_code_sample_docstrings,
|
41 |
add_start_docstrings,
|
42 |
add_start_docstrings_to_model_forward,
|
|
|
43 |
is_flash_attn_greater_or_equal_2_10,
|
44 |
logging,
|
45 |
replace_return_docstrings,
|
|
|
48 |
from .image_embedding_phi3_v import Phi3ImageEmbedding
|
49 |
|
50 |
|
51 |
+
try:
|
52 |
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
53 |
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
54 |
|
55 |
_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
|
56 |
+
except ImportError:
|
57 |
+
pass
|
58 |
|
59 |
logger = logging.get_logger(__name__)
|
60 |
|
|
|
1001 |
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
1002 |
model's internal embedding lookup matrix.
|
1003 |
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
|
1004 |
+
The tensors corresponding to the input images. Pixel values can be obtained using [`AutoImageProcessor`].
|
1005 |
+
See [`Phi3ImageProcessor.__call__`] for details.
|
1006 |
image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`, *optional*):
|
1007 |
The sizes of the images in the batch, being (height, width) for each image.
|
1008 |
use_cache (`bool`, *optional*):
|
|
|
1047 |
**config.embd_layer
|
1048 |
}
|
1049 |
self.vision_embed_tokens = Phi3ImageEmbedding(config, wte=self.embed_tokens, **embedding_config)
|
1050 |
+
# # set wte the same for vision embedding
|
1051 |
# self.vision_embed_tokens.wte.weight = self.embed_tokens.weight
|
1052 |
|
1053 |
self.layers = nn.ModuleList(
|
|
|
1630 |
logits=logits,
|
1631 |
hidden_states=model_outputs.hidden_states,
|
1632 |
attentions=model_outputs.attentions,
|
1633 |
+
)
|
sample_inference.py
ADDED
@@ -0,0 +1,129 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
|
2 |
+
|
3 |
+
from PIL import Image
|
4 |
+
import requests
|
5 |
+
import torch
|
6 |
+
from transformers import AutoModelForCausalLM
|
7 |
+
from transformers import AutoProcessor
|
8 |
+
model_path = "./"
|
9 |
+
|
10 |
+
kwargs = {}
|
11 |
+
kwargs['torch_dtype'] = torch.bfloat16
|
12 |
+
|
13 |
+
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
|
14 |
+
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype="auto").cuda()
|
15 |
+
|
16 |
+
user_prompt = '<|user|>\n'
|
17 |
+
assistant_prompt = '<|assistant|>\n'
|
18 |
+
prompt_suffix = "<|end|>\n"
|
19 |
+
|
20 |
+
#################################################### text-only ####################################################
|
21 |
+
# single-image prompt
|
22 |
+
prompt = f"{user_prompt}what is the answer for 1+1? Explain it.{prompt_suffix}{assistant_prompt}"
|
23 |
+
print(f">>> Prompt\n{prompt}")
|
24 |
+
inputs = processor(prompt, images=None, return_tensors="pt").to("cuda:0")
|
25 |
+
generate_ids = model.generate(**inputs,
|
26 |
+
max_new_tokens=1000,
|
27 |
+
eos_token_id=processor.tokenizer.eos_token_id,
|
28 |
+
)
|
29 |
+
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
|
30 |
+
response = processor.batch_decode(generate_ids,
|
31 |
+
skip_special_tokens=True,
|
32 |
+
clean_up_tokenization_spaces=False)[0]
|
33 |
+
print(f'>>> Response\n{response}')
|
34 |
+
|
35 |
+
#################################################### text-only 2 ####################################################
|
36 |
+
# single-image prompt
|
37 |
+
prompt = f"{user_prompt}Give me the code for sloving two-sum problem.{prompt_suffix}{assistant_prompt}"
|
38 |
+
print(f">>> Prompt\n{prompt}")
|
39 |
+
inputs = processor(prompt, images=None, return_tensors="pt").to("cuda:0")
|
40 |
+
generate_ids = model.generate(**inputs,
|
41 |
+
max_new_tokens=1000,
|
42 |
+
eos_token_id=processor.tokenizer.eos_token_id,
|
43 |
+
)
|
44 |
+
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
|
45 |
+
response = processor.batch_decode(generate_ids,
|
46 |
+
skip_special_tokens=True,
|
47 |
+
clean_up_tokenization_spaces=False)[0]
|
48 |
+
print(f'>>> Response\n{response}')
|
49 |
+
|
50 |
+
|
51 |
+
#################################################### EXAMPLE 1 ####################################################
|
52 |
+
# single-image prompt
|
53 |
+
prompt = f"{user_prompt}<|image_1|>\nWhat is shown in this image?{prompt_suffix}{assistant_prompt}"
|
54 |
+
url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
55 |
+
print(f">>> Prompt\n{prompt}")
|
56 |
+
image = Image.open(requests.get(url, stream=True).raw)
|
57 |
+
inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
|
58 |
+
generate_ids = model.generate(**inputs,
|
59 |
+
max_new_tokens=1000,
|
60 |
+
eos_token_id=processor.tokenizer.eos_token_id,
|
61 |
+
)
|
62 |
+
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
|
63 |
+
response = processor.batch_decode(generate_ids,
|
64 |
+
skip_special_tokens=True,
|
65 |
+
clean_up_tokenization_spaces=False)[0]
|
66 |
+
print(f'>>> Response\n{response}')
|
67 |
+
|
68 |
+
#################################################### EXAMPLE 2 ####################################################
|
69 |
+
# multiple image prompt
|
70 |
+
# Note: image tokens must start from <|image_1|>
|
71 |
+
prompt = f"{user_prompt}<|image_1|>\n<|image_2|>\n What is shown in this two images?{prompt_suffix}{assistant_prompt}"
|
72 |
+
print(f">>> Prompt\n{prompt}")
|
73 |
+
url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
74 |
+
image_1 = Image.open(requests.get(url, stream=True).raw)
|
75 |
+
url = "https://img.freepik.com/free-photo/painting-mountain-lake-with-mountain-background_188544-9126.jpg?w=2000"
|
76 |
+
image_2 = Image.open(requests.get(url, stream=True).raw)
|
77 |
+
images = [image_1, image_2]
|
78 |
+
inputs = processor(prompt, images, return_tensors="pt").to("cuda:0")
|
79 |
+
generate_ids = model.generate(**inputs,
|
80 |
+
max_new_tokens=1000,
|
81 |
+
eos_token_id=processor.tokenizer.eos_token_id,
|
82 |
+
)
|
83 |
+
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
|
84 |
+
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
85 |
+
print(f'>>> Response\n{response}')
|
86 |
+
|
87 |
+
#################################################### EXAMPLE 3 ####################################################
|
88 |
+
# chat template
|
89 |
+
chat = [
|
90 |
+
{"role": "user", "content": "<|image_1|>\nWhat is shown in this image?"},
|
91 |
+
{"role": "assistant", "content": "The image depicts a street scene with a prominent red stop sign in the foreground. The background showcases a building with traditional Chinese architecture, characterized by its red roof and ornate decorations. There are also several statues of lions, which are common in Chinese culture, positioned in front of the building. The street is lined with various shops and businesses, and there's a car passing by."},
|
92 |
+
{"role": "user", "content": "What is so special about this image"}
|
93 |
+
]
|
94 |
+
url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
95 |
+
image = Image.open(requests.get(url, stream=True).raw)
|
96 |
+
prompt = processor.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
|
97 |
+
# need to remove last <|endoftext|> if it is there, which is used for training, not inference. For training, make sure to add <|endoftext|> in the end.
|
98 |
+
if prompt.endswith("<|endoftext|>"):
|
99 |
+
prompt = prompt.rstrip("<|endoftext|>")
|
100 |
+
|
101 |
+
print(f">>> Prompt\n{prompt}")
|
102 |
+
|
103 |
+
inputs = processor(prompt, [image], return_tensors="pt").to("cuda:0")
|
104 |
+
generate_ids = model.generate(**inputs,
|
105 |
+
max_new_tokens=1000,
|
106 |
+
eos_token_id=processor.tokenizer.eos_token_id,
|
107 |
+
)
|
108 |
+
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
|
109 |
+
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
110 |
+
print(f'>>> Response\n{response}')
|
111 |
+
|
112 |
+
|
113 |
+
############################# to markdown #############################
|
114 |
+
# single-image prompt
|
115 |
+
prompt = f"{user_prompt}<|image_1|>\nCan you convert the table to markdown format?{prompt_suffix}{assistant_prompt}"
|
116 |
+
url = "https://support.content.office.net/en-us/media/3dd2b79b-9160-403d-9967-af893d17b580.png"
|
117 |
+
image = Image.open(requests.get(url, stream=True).raw)
|
118 |
+
inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
|
119 |
+
|
120 |
+
print(f">>> Prompt\n{prompt}")
|
121 |
+
generate_ids = model.generate(**inputs,
|
122 |
+
max_new_tokens=1000,
|
123 |
+
eos_token_id=processor.tokenizer.eos_token_id,
|
124 |
+
)
|
125 |
+
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
|
126 |
+
response = processor.batch_decode(generate_ids,
|
127 |
+
skip_special_tokens=False,
|
128 |
+
clean_up_tokenization_spaces=False)[0]
|
129 |
+
print(f'>>> Response\n{response}')
|
sft_args.json
CHANGED
@@ -7,7 +7,7 @@
|
|
7 |
"additional_trainable_parameters": [],
|
8 |
"tuner_backend": "peft",
|
9 |
"template_type": "phi3-vl",
|
10 |
-
"output_dir": "D:\\_____NEW_NN\\LLM\\MiniCPM-V\\finetune\\output\\phi3-vision-128k-instruct\\
|
11 |
"add_output_dir_suffix": true,
|
12 |
"ddp_backend": null,
|
13 |
"ddp_find_unused_parameters": null,
|
@@ -45,21 +45,21 @@
|
|
45 |
"bnb_4bit_use_double_quant": true,
|
46 |
"bnb_4bit_quant_storage": null,
|
47 |
"lora_target_modules": [
|
48 |
-
"
|
49 |
-
"q_proj",
|
50 |
-
"out_proj",
|
51 |
-
"gate_up_proj",
|
52 |
"img_projection.0",
|
53 |
"qkv_proj",
|
54 |
-
"
|
|
|
55 |
"fc1",
|
56 |
-
"
|
|
|
57 |
"fc2",
|
58 |
-
"
|
59 |
-
"
|
|
|
60 |
],
|
61 |
-
"lora_rank":
|
62 |
-
"lora_alpha":
|
63 |
"lora_dropout_p": 0.05,
|
64 |
"lora_bias_trainable": "none",
|
65 |
"lora_modules_to_save": [],
|
@@ -115,24 +115,24 @@
|
|
115 |
"lisa_step_interval": 20,
|
116 |
"gradient_checkpointing": true,
|
117 |
"deepspeed": null,
|
118 |
-
"batch_size":
|
119 |
-
"eval_batch_size":
|
120 |
"num_train_epochs": 4,
|
121 |
"max_steps": -1,
|
122 |
"optim": "adamw_torch",
|
123 |
"adam_beta1": 0.9,
|
124 |
-
"adam_beta2": 0.
|
125 |
-
"learning_rate": 0.
|
126 |
"weight_decay": 0.1,
|
127 |
"gradient_accumulation_steps": 2,
|
128 |
"max_grad_norm": 0.5,
|
129 |
"predict_with_generate": false,
|
130 |
-
"lr_scheduler_type": "
|
131 |
"warmup_ratio": 0.05,
|
132 |
"eval_steps": 50,
|
133 |
-
"save_steps":
|
134 |
"save_only_model": false,
|
135 |
-
"save_total_limit":
|
136 |
"logging_steps": 5,
|
137 |
"dataloader_num_workers": 1,
|
138 |
"dataloader_pin_memory": true,
|
@@ -149,7 +149,7 @@
|
|
149 |
"use_flash_attn": null,
|
150 |
"ignore_args_error": false,
|
151 |
"check_model_is_latest": true,
|
152 |
-
"logging_dir": "D:\\_____NEW_NN\\LLM\\MiniCPM-V\\finetune\\output\\phi3-vision-128k-instruct\\
|
153 |
"report_to": [
|
154 |
"tensorboard"
|
155 |
],
|
@@ -205,5 +205,5 @@
|
|
205 |
"load_in_4bit": false,
|
206 |
"load_in_8bit": false,
|
207 |
"train_sampler_random": true,
|
208 |
-
"training_args": "Seq2SeqTrainingArguments(output_dir='D:\\\\_____NEW_NN\\\\LLM\\\\MiniCPM-V\\\\finetune\\\\output\\\\phi3-vision-128k-instruct\\\\
|
209 |
}
|
|
|
7 |
"additional_trainable_parameters": [],
|
8 |
"tuner_backend": "peft",
|
9 |
"template_type": "phi3-vl",
|
10 |
+
"output_dir": "D:\\_____NEW_NN\\LLM\\MiniCPM-V\\finetune\\output\\phi3-vision-128k-instruct\\v4-20240619-010346",
|
11 |
"add_output_dir_suffix": true,
|
12 |
"ddp_backend": null,
|
13 |
"ddp_find_unused_parameters": null,
|
|
|
45 |
"bnb_4bit_use_double_quant": true,
|
46 |
"bnb_4bit_quant_storage": null,
|
47 |
"lora_target_modules": [
|
48 |
+
"k_proj",
|
|
|
|
|
|
|
49 |
"img_projection.0",
|
50 |
"qkv_proj",
|
51 |
+
"down_proj",
|
52 |
+
"o_proj",
|
53 |
"fc1",
|
54 |
+
"q_proj",
|
55 |
+
"out_proj",
|
56 |
"fc2",
|
57 |
+
"gate_up_proj",
|
58 |
+
"img_projection.2",
|
59 |
+
"v_proj"
|
60 |
],
|
61 |
+
"lora_rank": 128,
|
62 |
+
"lora_alpha": 128,
|
63 |
"lora_dropout_p": 0.05,
|
64 |
"lora_bias_trainable": "none",
|
65 |
"lora_modules_to_save": [],
|
|
|
115 |
"lisa_step_interval": 20,
|
116 |
"gradient_checkpointing": true,
|
117 |
"deepspeed": null,
|
118 |
+
"batch_size": 2,
|
119 |
+
"eval_batch_size": 2,
|
120 |
"num_train_epochs": 4,
|
121 |
"max_steps": -1,
|
122 |
"optim": "adamw_torch",
|
123 |
"adam_beta1": 0.9,
|
124 |
+
"adam_beta2": 0.95,
|
125 |
+
"learning_rate": 0.00014,
|
126 |
"weight_decay": 0.1,
|
127 |
"gradient_accumulation_steps": 2,
|
128 |
"max_grad_norm": 0.5,
|
129 |
"predict_with_generate": false,
|
130 |
+
"lr_scheduler_type": "cosine",
|
131 |
"warmup_ratio": 0.05,
|
132 |
"eval_steps": 50,
|
133 |
+
"save_steps": 389,
|
134 |
"save_only_model": false,
|
135 |
+
"save_total_limit": 8,
|
136 |
"logging_steps": 5,
|
137 |
"dataloader_num_workers": 1,
|
138 |
"dataloader_pin_memory": true,
|
|
|
149 |
"use_flash_attn": null,
|
150 |
"ignore_args_error": false,
|
151 |
"check_model_is_latest": true,
|
152 |
+
"logging_dir": "D:\\_____NEW_NN\\LLM\\MiniCPM-V\\finetune\\output\\phi3-vision-128k-instruct\\v4-20240619-010346/runs",
|
153 |
"report_to": [
|
154 |
"tensorboard"
|
155 |
],
|
|
|
205 |
"load_in_4bit": false,
|
206 |
"load_in_8bit": false,
|
207 |
"train_sampler_random": true,
|
208 |
+
"training_args": "Seq2SeqTrainingArguments(output_dir='D:\\\\_____NEW_NN\\\\LLM\\\\MiniCPM-V\\\\finetune\\\\output\\\\phi3-vision-128k-instruct\\\\v4-20240619-010346', overwrite_output_dir=False, do_train=False, do_eval=True, do_predict=False, evaluation_strategy=<IntervalStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=2, per_device_eval_batch_size=2, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=2, eval_accumulation_steps=None, eval_delay=0, learning_rate=0.00014, weight_decay=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, max_grad_norm=0.5, num_train_epochs=4, max_steps=-1, lr_scheduler_type=<SchedulerType.COSINE: 'cosine'>, lr_scheduler_kwargs={}, warmup_ratio=0.05, warmup_steps=0, log_level='passive', log_level_replica='warning', log_on_each_node=True, logging_dir='D:\\\\_____NEW_NN\\\\LLM\\\\MiniCPM-V\\\\finetune\\\\output\\\\phi3-vision-128k-instruct\\\\v4-20240619-010346/runs', logging_strategy=<IntervalStrategy.STEPS: 'steps'>, logging_first_step=True, logging_steps=5, logging_nan_inf_filter=True, save_strategy=<IntervalStrategy.STEPS: 'steps'>, save_steps=389, save_total_limit=8, save_safetensors=True, save_on_each_node=True, save_only_model=False, no_cuda=False, use_cpu=False, use_mps_device=False, seed=42, data_seed=None, jit_mode_eval=False, use_ipex=False, bf16=True, fp16=False, fp16_opt_level='O1', half_precision_backend='auto', bf16_full_eval=False, fp16_full_eval=False, tf32=None, local_rank=0, ddp_backend=None, tpu_num_cores=None, tpu_metrics_debug=False, debug=[], dataloader_drop_last=False, eval_steps=50, dataloader_num_workers=1, dataloader_prefetch_factor=None, past_index=-1, run_name='D:\\\\_____NEW_NN\\\\LLM\\\\MiniCPM-V\\\\finetune\\\\output\\\\phi3-vision-128k-instruct\\\\v4-20240619-010346', disable_tqdm=False, remove_unused_columns=False, label_names=None, load_best_model_at_end=False, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, fsdp=[], fsdp_min_num_params=0, fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, fsdp_transformer_layer_cls_to_wrap=None, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=None, even_batches=True, use_seedable_sampler=True, gradient_accumulation_kwargs=None), deepspeed=None, label_smoothing_factor=0.0, optim=<OptimizerNames.ADAMW_TORCH: 'adamw_torch'>, optim_args=None, adafactor=False, group_by_length=False, length_column_name='length', report_to=['tensorboard'], ddp_find_unused_parameters=None, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=None, dataloader_pin_memory=True, dataloader_persistent_workers=False, skip_memory_metrics=True, use_legacy_prediction_loop=False, push_to_hub=False, resume_from_checkpoint=None, hub_model_id=None, hub_strategy=<HubStrategy.EVERY_SAVE: 'every_save'>, hub_token=None, hub_private_repo=False, hub_always_push=False, gradient_checkpointing=True, gradient_checkpointing_kwargs=None, include_inputs_for_metrics=False, eval_do_concat_batches=True, fp16_backend='auto', push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=None, mp_parameters='', auto_find_batch_size=False, full_determinism=False, torchdynamo=None, ray_scope='last', ddp_timeout=1800, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, dispatch_batches=None, split_batches=None, include_tokens_per_second=False, include_num_input_tokens_seen=False, neftune_noise_alpha=None, optim_target_modules=None, sortish_sampler=True, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=GenerationConfig {\n \"do_sample\": true,\n \"eos_token_id\": 32000,\n \"max_new_tokens\": 2048,\n \"pad_token_id\": 32000,\n \"temperature\": 0.3,\n \"top_k\": 20,\n \"top_p\": 0.7\n}\n, train_sampler_random=True, push_hub_strategy='push_best', acc_strategy='token', additional_saved_files=[], metric_warmup_step=0, train_dataset_sample=797)"
|
209 |
}
|