Add files using upload-large-folder tool
Browse files- README.md +113 -0
- chat_template.json +3 -0
- config.json +150 -0
- generation_config.json +7 -0
- preprocessor_config.json +21 -0
- pytorch_model-00001-of-00002.bin +3 -0
- pytorch_model-00002-of-00002.bin +3 -0
- pytorch_model.bin.index.json +600 -0
- special_tokens_map.json +36 -0
- tokenizer.json +0 -0
- tokenizer_config.json +415 -0
README.md
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## Model Summary
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BigDocs-Phi-3.5-instruct is a multi-modal model that is trained with BigDocs for document intelligence tasks.
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microsoft/Phi-3.5-vision-instruct is used as the base and we perform 2 stages of training -
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1. Continual Pre-Training (CPT) with BigDocs-CPT keeping the encoder and adapter trainable.
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2. Fine Tuning (FT) with DocDownstream-1.0 keeping the decoder and adapter trainable.
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## General Document Benchmarks
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Models trained on [BigDocs-7.5M+DocDownstream] perform competitively across multimodal document benchmarks. We compare them to base checkpoints, instruction-tuned models, and those trained on [DocStruct4M+DocDownstream]. BigDocs models show consistent performance.
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| **Model** | **DocVQA**<br>*VAL* | **InfoVQA**<br>*VAL* | **DeepForm**<br>*TEST* | **KLC**<br>*TEST* | **WTQ**<br>*TEST* | **TabFact**<br>*TEST* | **ChartQA**<br>*TEST* | **TextVQA**<br>*VAL* | **MMMU**<br>*VAL* | **DudeMini**<br>*TEST* | **SlideVQA-M**<br>*TEST* | **TableVQA**<br>*TEST* | **Avg. Score** |
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|-----------------------------------|---------------------|-----------------------|-------------------------|-------------------|-------------------|-----------------------|-----------------------|----------------------|------------------|------------------------|--------------------------|-------------------------|----------------|
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| DocOwl1.5-8B (instruct) | 80.73 | 49.94 | 68.84 | 37.99 | 38.87 | 79.67 | 68.56 | 68.91 | 33.67 | 34.64 | 31.62 | 52.60 | 53.84 |
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| DocOwl1.5-8B (base) | 2.07 | 1.84 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 24.44 | 19.07 | 3.30 | 13.63 | 5.36 |
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| DocOwl1.5-8B (base) + DocStruct4M | 75.99 | 46.88 | 62.77 | 35.21 | 32.86 | 71.56 | 68.36 | 65.08 | 33.67 | 29.00 | 27.03 | 46.27 | 49.56 |
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| DocOwl1.5-8B (base) + BigDocs (Ours) | 78.70 | 47.62 | 64.39 | 36.93 | 35.69 | 72.65 | 65.80 | 67.30 | 32.33 | 32.55 | 29.60 | 49.03 | 51.05 |
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| Qwen2-VL-2B (instruct) | 89.16 | 64.11 | 32.38 | 25.18 | 38.20 | 57.21 | 73.40 | 79.90 | 42.00 | 45.23 | 46.50 | 43.07 | 53.03 |
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| Qwen2-VL-2B (base) | 7.26 | 0.78 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.14 | 34.89 | 28.43 | 14.55 | 0.00 | 7.25 |
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| Qwen2-VL-2B (base) + DocStruct4M | 59.53 | 32.00 | 53.98 | 36.38 | 28.48 | 64.24 | 54.44 | 55.89 | 34.89 | 28.78 | 22.68 | 46.53 | 43.15 |
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| *Qwen2-VL-2B (base) + BigDocs (Ours) | 57.23 | 31.88 | 49.31 | 34.39 | 31.61 | 64.75 | 68.60 | 61.01 | 35.67 | 27.19 | 17.46 | 47.53 | 43.89 |
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| Phi3.5-Vision-4B (instruct) | 86.00 | 56.20 | 10.47 | 7.49 | 17.18 | 30.43 | 82.16 | 73.12 | 46.00 | 37.20 | 30.93 | 70.70 | 45.66 |
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| Phi3.5-Vision-4B + DocStruct4M | 86.76 | 68.90 | 70.12 | 37.83 | 51.30 | 82.12 | 79.76 | 68.60 | 44.11 | 35.52 | 31.90 | 69.17 | 60.51 |
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| **Phi3.5-Vision-4B + BigDocs (Ours)** | **87.05** | **70.05** | **70.97** | **37.45** | **51.21** | **81.24** | **81.56** | **68.72** | **45.00** | **36.15** | **32.47** | **67.77** | **60.80** |
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| LLaVA-NeXT-7B (instruct) | 63.51 | 30.90 | 1.30 | 5.35 | 20.06 | 52.83 | 52.12 | 65.10 | 38.89 | 17.94 | 7.46 | 32.87 | 32.36 |
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| LLaVA-NeXT-7B + DocStruct4M | 60.95 | 26.14 | 39.78 | 28.34 | 25.90 | 67.72 | 61.20 | 52.25 | 25.78 | 21.70 | 15.33 | 27.03 | 37.68 |
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| LLaVA-NeXT-7B + BigDocs (Ours) | 57.13 | 24.47 | 46.38 | 31.09 | 27.06 | 72.58 | 54.72 | 49.06 | 17.78 | 22.88 | 16.07 | 33.13 | 37.70 |
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| Llama-3.2-90B | 74.15* | 48.71 | 4.18 | 1.81 | 24.20 | 63.01 | 11.36* | 71.69 | 57.78 | 41.24 | 26.09 | 41.57 | 38.82 |
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| GPT-4o 20240806 | 92.80 | 66.37 | 38.39 | 29.92 | 46.63 | 81.10 | 85.70 | 70.46 | 69.10 | 54.55 | 67.58 | 72.87 | 64.62 |
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| Claude-3.5 Sonnet | 88.48 | 59.05 | 31.41 | 24.82 | 47.13 | 53.48 | 51.84 | 71.42 | 64.78 | 35.11 | 0.00 | 81.27 | 50.73 |
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| GeminiPro-1.5 | 91.23 | 73.94 | 32.16 | 24.07 | 50.29 | 71.22 | 34.68 | 68.16 | 58.22 | 48.15 | 52.05 | 80.43 | 57.05 |
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| Qwen2-VL-72B | 96.50 | 84.50 | 30.45 | 24.78 | 55.63 | 0.00 | 88.30 | 85.50 | 64.50 | 35.87 | 2.15 | 74.23 | 58.40 |
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### Input Formats
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BigDocs-Phi-3.5-instruct follows the same chat format as Phi-3.5-vision-instruct:
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Single image:
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```
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<|user|>\n<|image_1|>\n{prompt}<|end|>\n<|assistant|>\n
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```
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Multi-turn conversations:
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```
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<|user|>\n<|image_1|>\n{prompt_1}<|end|>\n<|assistant|>\n{response_1}<|end|>\n<|user|>\n{prompt_2}<|end|>\n<|assistant|>\n
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```
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For multi-image usage, add multiple image placeholders in the front of the prompts. <|image_{}|> index should start from 1. One example of prompt is shown as follows:
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```
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<|user|>\n<|image_1|>\n<|image_2|>\n<|image_3|>\n<|image_4|>\n{prompt}<|end|>\n<|assistant|>\n
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```
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### Loading the model locally
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After obtaining the Phi-3.5-vision-instruct model checkpoints, users can use this sample code for inference.
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```python
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from PIL import Image
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import requests
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from transformers import AutoModelForCausalLM
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from transformers import AutoProcessor
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model_id = "BigDocs/BigDocs-Phi-3.5-instruct"
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# Note: set _attn_implementation='eager' if you don't have flash_attn installed
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="cuda",
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trust_remote_code=True,
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torch_dtype="auto",
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_attn_implementation='flash_attention_2'
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)
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# for best performance, use num_crops=4 for multi-frame, num_crops=16 for single-frame.
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processor = AutoProcessor.from_pretrained(model_id,
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trust_remote_code=True,
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num_crops=4
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)
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images = []
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placeholder = ""
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# Note: if OOM, you might consider reduce number of frames in this example.
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for i in range(1,20):
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url = f"https://image.slidesharecdn.com/azureintroduction-191206101932/75/Introduction-to-Microsoft-Azure-Cloud-{i}-2048.jpg"
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images.append(Image.open(requests.get(url, stream=True).raw))
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placeholder += f"<|image_{i}|>\n"
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messages = [
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{"role": "user", "content": placeholder+"Summarize the deck of slides."},
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]
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prompt = processor.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = processor(prompt, images, return_tensors="pt").to("cuda:0")
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generation_args = {
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"max_new_tokens": 1000,
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"temperature": 0.0,
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"do_sample": False,
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}
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generate_ids = model.generate(**inputs,
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eos_token_id=processor.tokenizer.eos_token_id,
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**generation_args
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)
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# remove input tokens
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generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
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response = processor.batch_decode(generate_ids,
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False)[0]
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print(response)
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```
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chat_template.json
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{
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"chat_template": "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}"
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}
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config.json
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{
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"_name_or_path": "microsoft/Phi-3.5-vision-instruct",
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"architectures": [
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"Phi3VForCausalLM"
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| 5 |
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],
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| 6 |
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"attention_dropout": 0.0,
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| 7 |
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"auto_map": {
|
| 8 |
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"AutoConfig": "microsoft/Phi-3.5-vision-instruct--configuration_phi3_v.Phi3VConfig",
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| 9 |
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"AutoModelForCausalLM": "microsoft/Phi-3.5-vision-instruct--modeling_phi3_v.Phi3VForCausalLM"
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| 10 |
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},
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| 11 |
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"bos_token_id": 1,
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| 12 |
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"embd_layer": {
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| 13 |
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"embedding_cls": "image",
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| 14 |
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"hd_transform_order": "sub_glb",
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| 15 |
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"projection_cls": "mlp",
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| 16 |
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"use_hd_transform": true,
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| 17 |
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"with_learnable_separator": true
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| 18 |
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},
|
| 19 |
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"embd_pdrop": 0.0,
|
| 20 |
+
"eos_token_id": 2,
|
| 21 |
+
"hidden_act": "silu",
|
| 22 |
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"hidden_size": 3072,
|
| 23 |
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"img_processor": {
|
| 24 |
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"image_dim_out": 1024,
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| 25 |
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"model_name": "openai/clip-vit-large-patch14-336",
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| 26 |
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"name": "clip_vision_model",
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| 27 |
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"num_img_tokens": 144
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| 28 |
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},
|
| 29 |
+
"initializer_range": 0.02,
|
| 30 |
+
"intermediate_size": 8192,
|
| 31 |
+
"max_position_embeddings": 131072,
|
| 32 |
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"model_type": "phi3_v",
|
| 33 |
+
"num_attention_heads": 32,
|
| 34 |
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"num_hidden_layers": 32,
|
| 35 |
+
"num_key_value_heads": 32,
|
| 36 |
+
"original_max_position_embeddings": 4096,
|
| 37 |
+
"pad_token_id": 32000,
|
| 38 |
+
"resid_pdrop": 0.0,
|
| 39 |
+
"rms_norm_eps": 1e-05,
|
| 40 |
+
"rope_scaling": {
|
| 41 |
+
"long_factor": [
|
| 42 |
+
1.0800000429153442,
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| 43 |
+
1.1100000143051147,
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| 44 |
+
1.1399999856948853,
|
| 45 |
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1.340000033378601,
|
| 46 |
+
1.5899999141693115,
|
| 47 |
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1.600000023841858,
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| 48 |
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1.6200000047683716,
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| 49 |
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2.620000123977661,
|
| 50 |
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3.2300000190734863,
|
| 51 |
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3.2300000190734863,
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| 52 |
+
4.789999961853027,
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| 53 |
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7.400000095367432,
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| 54 |
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7.700000286102295,
|
| 55 |
+
9.09000015258789,
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| 56 |
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12.199999809265137,
|
| 57 |
+
17.670000076293945,
|
| 58 |
+
24.46000099182129,
|
| 59 |
+
28.57000160217285,
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| 60 |
+
30.420001983642578,
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| 61 |
+
30.840002059936523,
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| 62 |
+
32.590003967285156,
|
| 63 |
+
32.93000411987305,
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| 64 |
+
42.320003509521484,
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| 65 |
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44.96000289916992,
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| 66 |
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50.340003967285156,
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| 67 |
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50.45000457763672,
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| 68 |
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generation_config.json
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preprocessor_config.json
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|
| 574 |
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| 575 |
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|
| 576 |
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|
| 580 |
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| 581 |
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|
| 582 |
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|
| 583 |
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|
| 584 |
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|
| 585 |
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|
| 586 |
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|
| 587 |
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|
| 588 |
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|
| 589 |
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|
| 590 |
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"model.vision_embed_tokens.img_processor.vision_model.post_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
| 591 |
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"model.vision_embed_tokens.img_processor.vision_model.pre_layrnorm.bias": "pytorch_model-00001-of-00002.bin",
|
| 592 |
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"model.vision_embed_tokens.img_processor.vision_model.pre_layrnorm.weight": "pytorch_model-00001-of-00002.bin",
|
| 593 |
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"model.vision_embed_tokens.img_projection.0.bias": "pytorch_model-00001-of-00002.bin",
|
| 594 |
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"model.vision_embed_tokens.img_projection.0.weight": "pytorch_model-00001-of-00002.bin",
|
| 595 |
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"model.vision_embed_tokens.img_projection.2.bias": "pytorch_model-00001-of-00002.bin",
|
| 596 |
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"model.vision_embed_tokens.img_projection.2.weight": "pytorch_model-00001-of-00002.bin",
|
| 597 |
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"model.vision_embed_tokens.sub_GN": "pytorch_model-00001-of-00002.bin",
|
| 598 |
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"model.vision_embed_tokens.wte.weight": "pytorch_model-00001-of-00002.bin"
|
| 599 |
+
}
|
| 600 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,36 @@
|
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|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<|system|>",
|
| 4 |
+
"<|end|>",
|
| 5 |
+
"<|user|>",
|
| 6 |
+
"<|end|>"
|
| 7 |
+
],
|
| 8 |
+
"bos_token": {
|
| 9 |
+
"content": "<s>",
|
| 10 |
+
"lstrip": false,
|
| 11 |
+
"normalized": false,
|
| 12 |
+
"rstrip": false,
|
| 13 |
+
"single_word": false
|
| 14 |
+
},
|
| 15 |
+
"eos_token": {
|
| 16 |
+
"content": "<|endoftext|>",
|
| 17 |
+
"lstrip": false,
|
| 18 |
+
"normalized": false,
|
| 19 |
+
"rstrip": false,
|
| 20 |
+
"single_word": false
|
| 21 |
+
},
|
| 22 |
+
"pad_token": {
|
| 23 |
+
"content": "<|endoftext|>",
|
| 24 |
+
"lstrip": false,
|
| 25 |
+
"normalized": false,
|
| 26 |
+
"rstrip": false,
|
| 27 |
+
"single_word": false
|
| 28 |
+
},
|
| 29 |
+
"unk_token": {
|
| 30 |
+
"content": "<unk>",
|
| 31 |
+
"lstrip": false,
|
| 32 |
+
"normalized": false,
|
| 33 |
+
"rstrip": false,
|
| 34 |
+
"single_word": false
|
| 35 |
+
}
|
| 36 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,415 @@
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|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"add_prefix_space": null,
|
| 5 |
+
"added_tokens_decoder": {
|
| 6 |
+
"0": {
|
| 7 |
+
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|
| 8 |
+
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|
| 9 |
+
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|
| 10 |
+
"rstrip": false,
|
| 11 |
+
"single_word": false,
|
| 12 |
+
"special": true
|
| 13 |
+
},
|
| 14 |
+
"1": {
|
| 15 |
+
"content": "<s>",
|
| 16 |
+
"lstrip": false,
|
| 17 |
+
"normalized": false,
|
| 18 |
+
"rstrip": false,
|
| 19 |
+
"single_word": false,
|
| 20 |
+
"special": true
|
| 21 |
+
},
|
| 22 |
+
"2": {
|
| 23 |
+
"content": "</s>",
|
| 24 |
+
"lstrip": false,
|
| 25 |
+
"normalized": false,
|
| 26 |
+
"rstrip": true,
|
| 27 |
+
"single_word": false,
|
| 28 |
+
"special": false
|
| 29 |
+
},
|
| 30 |
+
"32000": {
|
| 31 |
+
"content": "<|endoftext|>",
|
| 32 |
+
"lstrip": false,
|
| 33 |
+
"normalized": false,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false,
|
| 36 |
+
"special": true
|
| 37 |
+
},
|
| 38 |
+
"32001": {
|
| 39 |
+
"content": "<|assistant|>",
|
| 40 |
+
"lstrip": false,
|
| 41 |
+
"normalized": false,
|
| 42 |
+
"rstrip": true,
|
| 43 |
+
"single_word": false,
|
| 44 |
+
"special": true
|
| 45 |
+
},
|
| 46 |
+
"32002": {
|
| 47 |
+
"content": "<|placeholder1|>",
|
| 48 |
+
"lstrip": false,
|
| 49 |
+
"normalized": false,
|
| 50 |
+
"rstrip": true,
|
| 51 |
+
"single_word": false,
|
| 52 |
+
"special": true
|
| 53 |
+
},
|
| 54 |
+
"32003": {
|
| 55 |
+
"content": "<|placeholder2|>",
|
| 56 |
+
"lstrip": false,
|
| 57 |
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"normalized": false,
|
| 58 |
+
"rstrip": true,
|
| 59 |
+
"single_word": false,
|
| 60 |
+
"special": true
|
| 61 |
+
},
|
| 62 |
+
"32004": {
|
| 63 |
+
"content": "<|placeholder3|>",
|
| 64 |
+
"lstrip": false,
|
| 65 |
+
"normalized": false,
|
| 66 |
+
"rstrip": true,
|
| 67 |
+
"single_word": false,
|
| 68 |
+
"special": true
|
| 69 |
+
},
|
| 70 |
+
"32005": {
|
| 71 |
+
"content": "<|placeholder4|>",
|
| 72 |
+
"lstrip": false,
|
| 73 |
+
"normalized": false,
|
| 74 |
+
"rstrip": true,
|
| 75 |
+
"single_word": false,
|
| 76 |
+
"special": true
|
| 77 |
+
},
|
| 78 |
+
"32006": {
|
| 79 |
+
"content": "<|system|>",
|
| 80 |
+
"lstrip": false,
|
| 81 |
+
"normalized": false,
|
| 82 |
+
"rstrip": false,
|
| 83 |
+
"single_word": false,
|
| 84 |
+
"special": true
|
| 85 |
+
},
|
| 86 |
+
"32007": {
|
| 87 |
+
"content": "<|end|>",
|
| 88 |
+
"lstrip": false,
|
| 89 |
+
"normalized": false,
|
| 90 |
+
"rstrip": false,
|
| 91 |
+
"single_word": false,
|
| 92 |
+
"special": true
|
| 93 |
+
},
|
| 94 |
+
"32008": {
|
| 95 |
+
"content": "<|placeholder5|>",
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}
|