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LLM2CLIP-Llama3.1-8B-siglip2-so400m-patch14-224

Zero-Shot Classification
Safetensors
CLIP
LLM2CLIP
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  1. .gitattributes +2 -0
  2. CLIP.png +0 -0
  3. LLM2CLIP-Llama-3.1-8B/README.md +202 -0
  4. LLM2CLIP-Llama-3.1-8B/adapter_config.json +37 -0
  5. LLM2CLIP-Llama-3.1-8B/adapter_model.safetensors +3 -0
  6. LLM2CLIP-Llama-3.1-8B/llm2vec_config.json +6 -0
  7. LLM2CLIP-Llama-3.1-8B/special_tokens_map.json +17 -0
  8. LLM2CLIP-Llama-3.1-8B/tokenizer.json +0 -0
  9. LLM2CLIP-Llama-3.1-8B/tokenizer_config.json +2063 -0
  10. LLM2CLIP-Llama-3.1-8B/trainer_state.json +2182 -0
  11. LLM2CLIP-Llama-3.1-8B/training_args.bin +3 -0
  12. LLM2CLIP-SigLIP2-So-14-224.pt +3 -0
  13. README.md +79 -3
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+ LLM2CLIP-SigLIP2-So-14-224.pt filter=lfs diff=lfs merge=lfs -text
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+ LLM2CLIP-Llama-3.1-8B filter=lfs diff=lfs merge=lfs -text
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+ ---
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+ base_model: meta-llama/Llama-3.1-8B-Instruct
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+ library_name: peft
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+ ## How to Get Started with the Model
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+ ### Framework versions
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+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
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+ "clean_up_tokenization_spaces": true,
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+ "tokenizer_class": "PreTrainedTokenizerFast"
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+ }
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1
- ---
2
- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - CLIP
5
+ - LLM2CLIP
6
+ pipeline_tag: zero-shot-classification
7
+ ---
8
+ <div align="center">
9
+
10
+ <h2><a href="">LLM2CLIP: Extending the Capability Boundaries of CLIP through Large Language Models</a></h2>
11
+ Weiquan Huang<sup>1*</sup>, Aoqi Wu<sup>1*</sup>, Yifan Yang<sup>2†</sup>, Xufang Luo<sup>2</sup>, Yuqing Yang<sup>2</sup>, Liang Hu<sup>1</sup>, Qi Dai<sup>2</sup>, Xiyang Dai<sup>2</sup>, Dongdong Chen<sup>2</sup>, Chong Luo<sup>2</sup>, Lili Qiu<sup>2</sup>
12
+
13
+ <sup>1</sup>Tongji Universiy, <sup>2</sup>Microsoft Corporation <br><sup>*</sup>Equal contribution <br><sup>†</sup> Corresponding to: yifanyang@microsoft.com
14
+
15
+ <p><a rel="nofollow" href="https://github.com/microsoft/LLM2CLIP">[📂 GitHub]</a> <a rel="nofollow" href="https://microsoft.github.io/LLM2CLIP/">[🆕 Blog]</a> <a rel="nofollow" href="">[📜 LLM2CLIP]</a>
16
+ </div>
17
+
18
+
19
+ In this paper, we propose LLM2CLIP, a novel approach that embraces the power of LLMs to unlock CLIP’s potential. By fine-tuning the LLM in the caption space with contrastive learning, we extract its textual capabilities into the output embeddings, significantly improving the output layer’s textual discriminability. We then design an efficient training process where the fine-tuned LLM acts as a powerful teacher for CLIP’s visual encoder. Thanks to the LLM’s presence, we can now incorporate longer and more complex captions without being restricted by vanilla CLIP text encoder’s context window and ability limitations. Our experiments demonstrate that this approach brings substantial improvements in cross-modal tasks. Our method directly boosted the performance of the previously SOTA EVA02 model by 16.5% on both long-text and short-text retrieval tasks, transforming a CLIP model trained solely on English data into a state-of-the-art cross-lingual model. Moreover, when integrated into mul- timodal training with models like Llava 1.5, it consistently outperformed CLIP across nearly all benchmarks, demonstrating comprehensive performance improvements.
20
+
21
+
22
+ ## Model Details
23
+ - **Model Type:** vision foundation model, feature backbone
24
+ - **Pretrain Dataset:** CC3M, CC12M, YFCC15M and LAION-2B(30M subset)
25
+
26
+
27
+ ## Usage
28
+ Go to [GitHub](https://github.com/microsoft/LLM2CLIP/tree/main/llm2clip)
29
+
30
+
31
+ ```bash
32
+ # put LLM2CLIP-Llama-3.1-8B into the checkpoints folder.
33
+ ln -s LLM2CLIP-Llama-3.1-8B llm2clip/checkpoints/
34
+
35
+ pip install flash_attn
36
+ pip install timm==1.0.15
37
+ pip uninstall -y llm2vec
38
+ pip install transformers==4.46
39
+ ```
40
+
41
+ ```python
42
+ import torch
43
+ from PIL import Image
44
+ from eva_clip import create_model_and_transforms
45
+
46
+ model, _, preprocess_val = create_model_and_transforms(
47
+ "Llama3.1-8B-Siglip2-So-L-14-224",force_custom_clip=True)
48
+ ckpt = torch.load("LLM2CLIP-SigLIP2-So-14-224.pt")
49
+ model.load_state_dict(ckpt, strict=False)
50
+ model = model.cuda().eval()
51
+
52
+ captions = ["a diagram", "a dog", "a cat"]
53
+ image = Image.open("CLIP.png")
54
+ image = preprocess_val(image).unsqueeze(0).cuda()
55
+
56
+ with torch.no_grad(), torch.cuda.amp.autocast():
57
+ image_features = model.encode_image(image)
58
+ text_features = model.encode_text(captions)
59
+
60
+ image_features /= image_features.norm(dim=-1, keepdim=True)
61
+ text_features /= text_features.norm(dim=-1, keepdim=True)
62
+
63
+ text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
64
+
65
+ print("Label probs:", text_probs)
66
+ ```
67
+
68
+ ## BibTeX & Citation
69
+
70
+ ```
71
+ @misc{huang2024llm2clippowerfullanguagemodel,
72
+ title={LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation},
73
+ author={Weiquan Huang and Aoqi Wu and Yifan Yang and Xufang Luo and Yuqing Yang and Liang Hu and Qi Dai and Xiyang Dai and Dongdong Chen and Chong Luo and Lili Qiu},
74
+ year={2024},
75
+ eprint={2411.04997},
76
+ archivePrefix={arXiv},
77
+ primaryClass={cs.CV},
78
+ url={https://arxiv.org/abs/2411.04997},
79
+ }