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Browse files- README.md +130 -3
- added_tokens.json +11 -0
- config.json +200 -0
- generation_config.json +4 -0
- model-00001-of-00011.safetensors +3 -0
- model-00002-of-00011.safetensors +3 -0
- model-00003-of-00011.safetensors +3 -0
- model-00004-of-00011.safetensors +3 -0
- model-00005-of-00011.safetensors +3 -0
- model-00006-of-00011.safetensors +3 -0
- model-00007-of-00011.safetensors +3 -0
- model-00008-of-00011.safetensors +3 -0
- model-00009-of-00011.safetensors +3 -0
- model-00010-of-00011.safetensors +3 -0
- model-00011-of-00011.safetensors +3 -0
- model.safetensors.index.json +941 -0
- special_tokens_map.json +41 -0
- tokenization_internlm2.py +235 -0
- tokenizer.model +3 -0
- tokenizer_config.json +173 -0
README.md
CHANGED
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---
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---
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---
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pipeline_tag: text-generation
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---
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## Citation
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arxiv.org/abs/2408.07246
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```
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@misc{li2024seeingunderstandingbridgingvision,
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title={Seeing and Understanding: Bridging Vision with Chemical Knowledge Via ChemVLM},
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author={Junxian Li and Di Zhang and Xunzhi Wang and Zeying Hao and Jingdi Lei and Qian Tan and Cai Zhou and Wei Liu and Weiyun Wang and Zhe Chen and Wenhai Wang and Wei Li and Shufei Zhang and Mao Su and Wanli Ouyang and Yuqiang Li and Dongzhan Zhou},
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year={2024},
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eprint={2408.07246},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2408.07246},
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}
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```
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Quick start as below(```transformers>=4.37.0 is needed```)
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```Python
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from transformers import AutoTokenizer, AutoModelforCasualLM
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import torch
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import torchvision.transforms as T
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import transformers
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from torchvision.transforms.functional import InterpolationMode
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IMAGENET_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_STD = (0.229, 0.224, 0.225)
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IMAGENET_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_STD = (0.229, 0.224, 0.225)
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def build_transform(input_size):
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MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
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transform = T.Compose([
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T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
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T.ToTensor(),
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T.Normalize(mean=MEAN, std=STD)
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])
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return transform
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def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
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best_ratio_diff = float('inf')
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best_ratio = (1, 1)
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area = width * height
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for ratio in target_ratios:
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target_aspect_ratio = ratio[0] / ratio[1]
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ratio_diff = abs(aspect_ratio - target_aspect_ratio)
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if ratio_diff < best_ratio_diff:
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best_ratio_diff = ratio_diff
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best_ratio = ratio
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elif ratio_diff == best_ratio_diff:
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
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best_ratio = ratio
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return best_ratio
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def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
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orig_width, orig_height = image.size
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aspect_ratio = orig_width / orig_height
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# calculate the existing image aspect ratio
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target_ratios = set(
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(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
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i * j <= max_num and i * j >= min_num)
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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# find the closest aspect ratio to the target
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target_aspect_ratio = find_closest_aspect_ratio(
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aspect_ratio, target_ratios, orig_width, orig_height, image_size)
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# calculate the target width and height
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target_width = image_size * target_aspect_ratio[0]
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target_height = image_size * target_aspect_ratio[1]
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
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# resize the image
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resized_img = image.resize((target_width, target_height))
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processed_images = []
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for i in range(blocks):
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box = (
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(i % (target_width // image_size)) * image_size,
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(i // (target_width // image_size)) * image_size,
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((i % (target_width // image_size)) + 1) * image_size,
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((i // (target_width // image_size)) + 1) * image_size
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)
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# split the image
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split_img = resized_img.crop(box)
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processed_images.append(split_img)
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assert len(processed_images) == blocks
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if use_thumbnail and len(processed_images) != 1:
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thumbnail_img = image.resize((image_size, image_size))
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processed_images.append(thumbnail_img)
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return processed_images
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def load_image(image_file, input_size=448, max_num=6):
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image = Image.open(image_file).convert('RGB')
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transform = build_transform(input_size=input_size)
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images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
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pixel_values = [transform(image) for image in images]
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pixel_values = torch.stack(pixel_values)
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return pixel_values
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tokenizer = AutoTokenizer.from_pretrained('AI4Chem/ChemVLM-26B-1-2', trust_remote_code=True)
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query = "Please describe the molecule in the image."
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image_path = "your image path"
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pixel_values = load_image(image_path, max_num=6).to(torch.bfloat16).cuda()
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+
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+
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+
model = AutoModelForCausalLM.from_pretrained(
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"AI4Chem/ChemVLM-26B-1-2",
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True
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).to(device).eval().cuda()
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gen_kwargs = {"max_length": 1000, "do_sample": True, "temperature": 0.7, "top_p": 0.9}
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response = model.chat(tokenizer, pixel_values, query, gen_kwargs)
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```
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added_tokens.json
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{
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"</box>": 92552,
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"</img>": 92545,
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"</quad>": 92548,
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"</ref>": 92550,
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"<IMG_CONTEXT>": 92546,
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"<box>": 92551,
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"<img>": 92544,
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| 9 |
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"<quad>": 92547,
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"<ref>": 92549
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}
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config.json
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| 1 |
+
{
|
| 2 |
+
"_commit_hash": null,
|
| 3 |
+
"_name_or_path": "AI4Chem/ChemVLM-26B-1-2",
|
| 4 |
+
"architectures": [
|
| 5 |
+
"InternVLChatModel"
|
| 6 |
+
],
|
| 7 |
+
"auto_map": {
|
| 8 |
+
"AutoConfig": "configuration_internvl_chat.InternVLChatConfig",
|
| 9 |
+
"AutoModel": "modeling_internvl_chat.InternVLChatModel",
|
| 10 |
+
"AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"
|
| 11 |
+
},
|
| 12 |
+
"downsample_ratio": 0.5,
|
| 13 |
+
"dynamic_image_size": true,
|
| 14 |
+
"force_image_size": 448,
|
| 15 |
+
"llm_config": {
|
| 16 |
+
"_name_or_path": "internlm/internlm2-chat-20b",
|
| 17 |
+
"add_cross_attention": false,
|
| 18 |
+
"architectures": [
|
| 19 |
+
"InternLM2ForCausalLM"
|
| 20 |
+
],
|
| 21 |
+
"attn_implementation": "flash_attention_2",
|
| 22 |
+
"auto_map": {
|
| 23 |
+
"AutoConfig": "configuration_internlm2.InternLM2Config",
|
| 24 |
+
"AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
|
| 25 |
+
"AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
|
| 26 |
+
},
|
| 27 |
+
"bad_words_ids": null,
|
| 28 |
+
"begin_suppress_tokens": null,
|
| 29 |
+
"bias": false,
|
| 30 |
+
"bos_token_id": 1,
|
| 31 |
+
"chunk_size_feed_forward": 0,
|
| 32 |
+
"cross_attention_hidden_size": null,
|
| 33 |
+
"decoder_start_token_id": null,
|
| 34 |
+
"diversity_penalty": 0.0,
|
| 35 |
+
"do_sample": false,
|
| 36 |
+
"early_stopping": false,
|
| 37 |
+
"encoder_no_repeat_ngram_size": 0,
|
| 38 |
+
"eos_token_id": 2,
|
| 39 |
+
"exponential_decay_length_penalty": null,
|
| 40 |
+
"finetuning_task": null,
|
| 41 |
+
"forced_bos_token_id": null,
|
| 42 |
+
"forced_eos_token_id": null,
|
| 43 |
+
"hidden_act": "silu",
|
| 44 |
+
"hidden_size": 6144,
|
| 45 |
+
"id2label": {
|
| 46 |
+
"0": "LABEL_0",
|
| 47 |
+
"1": "LABEL_1"
|
| 48 |
+
},
|
| 49 |
+
"initializer_range": 0.02,
|
| 50 |
+
"intermediate_size": 16384,
|
| 51 |
+
"is_decoder": false,
|
| 52 |
+
"is_encoder_decoder": false,
|
| 53 |
+
"label2id": {
|
| 54 |
+
"LABEL_0": 0,
|
| 55 |
+
"LABEL_1": 1
|
| 56 |
+
},
|
| 57 |
+
"length_penalty": 1.0,
|
| 58 |
+
"max_length": 20,
|
| 59 |
+
"max_position_embeddings": 32768,
|
| 60 |
+
"min_length": 0,
|
| 61 |
+
"model_type": "internlm2",
|
| 62 |
+
"no_repeat_ngram_size": 0,
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+
"vision_model.encoder.layers.9.attn.q_norm.weight": "model-00001-of-00011.safetensors",
|
| 931 |
+
"vision_model.encoder.layers.9.attn.qkv.weight": "model-00001-of-00011.safetensors",
|
| 932 |
+
"vision_model.encoder.layers.9.ls1": "model-00001-of-00011.safetensors",
|
| 933 |
+
"vision_model.encoder.layers.9.ls2": "model-00001-of-00011.safetensors",
|
| 934 |
+
"vision_model.encoder.layers.9.mlp.fc1.bias": "model-00001-of-00011.safetensors",
|
| 935 |
+
"vision_model.encoder.layers.9.mlp.fc1.weight": "model-00001-of-00011.safetensors",
|
| 936 |
+
"vision_model.encoder.layers.9.mlp.fc2.bias": "model-00001-of-00011.safetensors",
|
| 937 |
+
"vision_model.encoder.layers.9.mlp.fc2.weight": "model-00001-of-00011.safetensors",
|
| 938 |
+
"vision_model.encoder.layers.9.norm1.weight": "model-00001-of-00011.safetensors",
|
| 939 |
+
"vision_model.encoder.layers.9.norm2.weight": "model-00001-of-00011.safetensors"
|
| 940 |
+
}
|
| 941 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,41 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<img>",
|
| 4 |
+
"</img>",
|
| 5 |
+
"<IMG_CONTEXT>",
|
| 6 |
+
"<quad>",
|
| 7 |
+
"</quad>",
|
| 8 |
+
"<ref>",
|
| 9 |
+
"</ref>",
|
| 10 |
+
"<box>",
|
| 11 |
+
"</box>"
|
| 12 |
+
],
|
| 13 |
+
"bos_token": {
|
| 14 |
+
"content": "<s>",
|
| 15 |
+
"lstrip": false,
|
| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false
|
| 19 |
+
},
|
| 20 |
+
"eos_token": {
|
| 21 |
+
"content": "</s>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false
|
| 26 |
+
},
|
| 27 |
+
"pad_token": {
|
| 28 |
+
"content": "</s>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false
|
| 33 |
+
},
|
| 34 |
+
"unk_token": {
|
| 35 |
+
"content": "<unk>",
|
| 36 |
+
"lstrip": false,
|
| 37 |
+
"normalized": false,
|
| 38 |
+
"rstrip": false,
|
| 39 |
+
"single_word": false
|
| 40 |
+
}
|
| 41 |
+
}
|
tokenization_internlm2.py
ADDED
|
@@ -0,0 +1,235 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
|
| 2 |
+
#
|
| 3 |
+
# This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
|
| 4 |
+
#
|
| 5 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 6 |
+
# you may not use this file except in compliance with the License.
|
| 7 |
+
# You may obtain a copy of the License at
|
| 8 |
+
#
|
| 9 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 10 |
+
#
|
| 11 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 12 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 13 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 14 |
+
# See the License for the specific language governing permissions and
|
| 15 |
+
# limitations under the License.
|
| 16 |
+
|
| 17 |
+
"""Tokenization classes for InternLM."""
|
| 18 |
+
import os
|
| 19 |
+
from shutil import copyfile
|
| 20 |
+
from typing import Any, Dict, List, Optional, Tuple
|
| 21 |
+
|
| 22 |
+
import sentencepiece as spm
|
| 23 |
+
from transformers.tokenization_utils import PreTrainedTokenizer
|
| 24 |
+
from transformers.utils import logging
|
| 25 |
+
|
| 26 |
+
logger = logging.get_logger(__name__)
|
| 27 |
+
|
| 28 |
+
VOCAB_FILES_NAMES = {'vocab_file': './tokenizer.model'}
|
| 29 |
+
|
| 30 |
+
PRETRAINED_VOCAB_FILES_MAP = {}
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
|
| 34 |
+
class InternLM2Tokenizer(PreTrainedTokenizer):
|
| 35 |
+
"""
|
| 36 |
+
Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
|
| 37 |
+
|
| 38 |
+
Args:
|
| 39 |
+
vocab_file (`str`):
|
| 40 |
+
Path to the vocabulary file.
|
| 41 |
+
"""
|
| 42 |
+
|
| 43 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
| 44 |
+
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
| 45 |
+
model_input_names = ['input_ids', 'attention_mask']
|
| 46 |
+
_auto_class = 'AutoTokenizer'
|
| 47 |
+
|
| 48 |
+
def __init__(
|
| 49 |
+
self,
|
| 50 |
+
vocab_file,
|
| 51 |
+
unk_token='<unk>',
|
| 52 |
+
bos_token='<s>',
|
| 53 |
+
eos_token='</s>',
|
| 54 |
+
pad_token='</s>',
|
| 55 |
+
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
| 56 |
+
add_bos_token=True,
|
| 57 |
+
add_eos_token=False,
|
| 58 |
+
decode_with_prefix_space=False,
|
| 59 |
+
clean_up_tokenization_spaces=False,
|
| 60 |
+
**kwargs,
|
| 61 |
+
):
|
| 62 |
+
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
| 63 |
+
self.vocab_file = vocab_file
|
| 64 |
+
self.add_bos_token = add_bos_token
|
| 65 |
+
self.add_eos_token = add_eos_token
|
| 66 |
+
self.decode_with_prefix_space = decode_with_prefix_space
|
| 67 |
+
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
| 68 |
+
self.sp_model.Load(vocab_file)
|
| 69 |
+
self._no_prefix_space_tokens = None
|
| 70 |
+
super().__init__(
|
| 71 |
+
bos_token=bos_token,
|
| 72 |
+
eos_token=eos_token,
|
| 73 |
+
unk_token=unk_token,
|
| 74 |
+
pad_token=pad_token,
|
| 75 |
+
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
| 76 |
+
**kwargs,
|
| 77 |
+
)
|
| 78 |
+
|
| 79 |
+
@property
|
| 80 |
+
def no_prefix_space_tokens(self):
|
| 81 |
+
if self._no_prefix_space_tokens is None:
|
| 82 |
+
vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
|
| 83 |
+
self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith('▁')}
|
| 84 |
+
return self._no_prefix_space_tokens
|
| 85 |
+
|
| 86 |
+
@property
|
| 87 |
+
def vocab_size(self):
|
| 88 |
+
"""Returns vocab size"""
|
| 89 |
+
return self.sp_model.get_piece_size()
|
| 90 |
+
|
| 91 |
+
@property
|
| 92 |
+
def bos_token_id(self) -> Optional[int]:
|
| 93 |
+
return self.sp_model.bos_id()
|
| 94 |
+
|
| 95 |
+
@property
|
| 96 |
+
def eos_token_id(self) -> Optional[int]:
|
| 97 |
+
return self.sp_model.eos_id()
|
| 98 |
+
|
| 99 |
+
def get_vocab(self):
|
| 100 |
+
"""Returns vocab as a dict"""
|
| 101 |
+
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
| 102 |
+
vocab.update(self.added_tokens_encoder)
|
| 103 |
+
return vocab
|
| 104 |
+
|
| 105 |
+
def _tokenize(self, text):
|
| 106 |
+
"""Returns a tokenized string."""
|
| 107 |
+
return self.sp_model.encode(text, out_type=str)
|
| 108 |
+
|
| 109 |
+
def _convert_token_to_id(self, token):
|
| 110 |
+
"""Converts a token (str) in an id using the vocab."""
|
| 111 |
+
return self.sp_model.piece_to_id(token)
|
| 112 |
+
|
| 113 |
+
def _convert_id_to_token(self, index):
|
| 114 |
+
"""Converts an index (integer) in a token (str) using the vocab."""
|
| 115 |
+
token = self.sp_model.IdToPiece(index)
|
| 116 |
+
return token
|
| 117 |
+
|
| 118 |
+
def _maybe_add_prefix_space(self, tokens, decoded):
|
| 119 |
+
if tokens and tokens[0] not in self.no_prefix_space_tokens:
|
| 120 |
+
return ' ' + decoded
|
| 121 |
+
else:
|
| 122 |
+
return decoded
|
| 123 |
+
|
| 124 |
+
def convert_tokens_to_string(self, tokens):
|
| 125 |
+
"""Converts a sequence of tokens (string) in a single string."""
|
| 126 |
+
current_sub_tokens = []
|
| 127 |
+
out_string = ''
|
| 128 |
+
prev_is_special = False
|
| 129 |
+
for token in tokens:
|
| 130 |
+
# make sure that special tokens are not decoded using sentencepiece model
|
| 131 |
+
if token in self.all_special_tokens:
|
| 132 |
+
if not prev_is_special:
|
| 133 |
+
out_string += ' '
|
| 134 |
+
out_string += self.sp_model.decode(current_sub_tokens) + token
|
| 135 |
+
prev_is_special = True
|
| 136 |
+
current_sub_tokens = []
|
| 137 |
+
else:
|
| 138 |
+
current_sub_tokens.append(token)
|
| 139 |
+
prev_is_special = False
|
| 140 |
+
out_string += self.sp_model.decode(current_sub_tokens)
|
| 141 |
+
out_string = self.clean_up_tokenization(out_string)
|
| 142 |
+
out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
|
| 143 |
+
return out_string[1:]
|
| 144 |
+
|
| 145 |
+
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
| 146 |
+
"""
|
| 147 |
+
Save the vocabulary and special tokens file to a directory.
|
| 148 |
+
|
| 149 |
+
Args:
|
| 150 |
+
save_directory (`str`):
|
| 151 |
+
The directory in which to save the vocabulary.
|
| 152 |
+
|
| 153 |
+
Returns:
|
| 154 |
+
`Tuple(str)`: Paths to the files saved.
|
| 155 |
+
"""
|
| 156 |
+
if not os.path.isdir(save_directory):
|
| 157 |
+
logger.error(f'Vocabulary path ({save_directory}) should be a directory')
|
| 158 |
+
return
|
| 159 |
+
out_vocab_file = os.path.join(
|
| 160 |
+
save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']
|
| 161 |
+
)
|
| 162 |
+
|
| 163 |
+
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
| 164 |
+
copyfile(self.vocab_file, out_vocab_file)
|
| 165 |
+
elif not os.path.isfile(self.vocab_file):
|
| 166 |
+
with open(out_vocab_file, 'wb') as fi:
|
| 167 |
+
content_spiece_model = self.sp_model.serialized_model_proto()
|
| 168 |
+
fi.write(content_spiece_model)
|
| 169 |
+
|
| 170 |
+
return (out_vocab_file,)
|
| 171 |
+
|
| 172 |
+
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
| 173 |
+
if self.add_bos_token:
|
| 174 |
+
bos_token_ids = [self.bos_token_id]
|
| 175 |
+
else:
|
| 176 |
+
bos_token_ids = []
|
| 177 |
+
|
| 178 |
+
output = bos_token_ids + token_ids_0
|
| 179 |
+
|
| 180 |
+
if token_ids_1 is not None:
|
| 181 |
+
output = output + token_ids_1
|
| 182 |
+
|
| 183 |
+
if self.add_eos_token:
|
| 184 |
+
output = output + [self.eos_token_id]
|
| 185 |
+
|
| 186 |
+
return output
|
| 187 |
+
|
| 188 |
+
def get_special_tokens_mask(
|
| 189 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
| 190 |
+
) -> List[int]:
|
| 191 |
+
"""
|
| 192 |
+
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
| 193 |
+
special tokens using the tokenizer `prepare_for_model` method.
|
| 194 |
+
|
| 195 |
+
Args:
|
| 196 |
+
token_ids_0 (`List[int]`):
|
| 197 |
+
List of IDs.
|
| 198 |
+
token_ids_1 (`List[int]`, *optional*):
|
| 199 |
+
Optional second list of IDs for sequence pairs.
|
| 200 |
+
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
| 201 |
+
Whether or not the token list is already formatted with special tokens for the model.
|
| 202 |
+
|
| 203 |
+
Returns:
|
| 204 |
+
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
| 205 |
+
"""
|
| 206 |
+
if already_has_special_tokens:
|
| 207 |
+
return super().get_special_tokens_mask(
|
| 208 |
+
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
| 209 |
+
)
|
| 210 |
+
|
| 211 |
+
if token_ids_1 is None:
|
| 212 |
+
return [1] + ([0] * len(token_ids_0)) + [1]
|
| 213 |
+
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
|
| 214 |
+
|
| 215 |
+
def create_token_type_ids_from_sequences(
|
| 216 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
| 217 |
+
) -> List[int]:
|
| 218 |
+
"""
|
| 219 |
+
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
|
| 220 |
+
use of token type ids, therefore a list of zeros is returned.
|
| 221 |
+
|
| 222 |
+
Args:
|
| 223 |
+
token_ids_0 (`List[int]`):
|
| 224 |
+
List of IDs.
|
| 225 |
+
token_ids_1 (`List[int]`, *optional*):
|
| 226 |
+
Optional second list of IDs for sequence pairs.
|
| 227 |
+
|
| 228 |
+
Returns:
|
| 229 |
+
`List[int]`: List of zeros.
|
| 230 |
+
"""
|
| 231 |
+
eos = [self.eos_token_id]
|
| 232 |
+
|
| 233 |
+
if token_ids_1 is None:
|
| 234 |
+
return len(token_ids_0 + eos) * [0]
|
| 235 |
+
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
|
tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
|
| 3 |
+
size 1477754
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,173 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "<unk>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"1": {
|
| 12 |
+
"content": "<s>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"2": {
|
| 20 |
+
"content": "</s>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"92538": {
|
| 28 |
+
"content": "<|plugin|>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"92539": {
|
| 36 |
+
"content": "<|interpreter|>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
},
|
| 43 |
+
"92540": {
|
| 44 |
+
"content": "<|action_end|>",
|
| 45 |
+
"lstrip": false,
|
| 46 |
+
"normalized": false,
|
| 47 |
+
"rstrip": false,
|
| 48 |
+
"single_word": false,
|
| 49 |
+
"special": true
|
| 50 |
+
},
|
| 51 |
+
"92541": {
|
| 52 |
+
"content": "<|action_start|>",
|
| 53 |
+
"lstrip": false,
|
| 54 |
+
"normalized": false,
|
| 55 |
+
"rstrip": false,
|
| 56 |
+
"single_word": false,
|
| 57 |
+
"special": true
|
| 58 |
+
},
|
| 59 |
+
"92542": {
|
| 60 |
+
"content": "<|im_end|>",
|
| 61 |
+
"lstrip": false,
|
| 62 |
+
"normalized": false,
|
| 63 |
+
"rstrip": false,
|
| 64 |
+
"single_word": false,
|
| 65 |
+
"special": true
|
| 66 |
+
},
|
| 67 |
+
"92543": {
|
| 68 |
+
"content": "<|im_start|>",
|
| 69 |
+
"lstrip": false,
|
| 70 |
+
"normalized": false,
|
| 71 |
+
"rstrip": false,
|
| 72 |
+
"single_word": false,
|
| 73 |
+
"special": true
|
| 74 |
+
},
|
| 75 |
+
"92544": {
|
| 76 |
+
"content": "<img>",
|
| 77 |
+
"lstrip": false,
|
| 78 |
+
"normalized": false,
|
| 79 |
+
"rstrip": false,
|
| 80 |
+
"single_word": false,
|
| 81 |
+
"special": true
|
| 82 |
+
},
|
| 83 |
+
"92545": {
|
| 84 |
+
"content": "</img>",
|
| 85 |
+
"lstrip": false,
|
| 86 |
+
"normalized": false,
|
| 87 |
+
"rstrip": false,
|
| 88 |
+
"single_word": false,
|
| 89 |
+
"special": true
|
| 90 |
+
},
|
| 91 |
+
"92546": {
|
| 92 |
+
"content": "<IMG_CONTEXT>",
|
| 93 |
+
"lstrip": false,
|
| 94 |
+
"normalized": false,
|
| 95 |
+
"rstrip": false,
|
| 96 |
+
"single_word": false,
|
| 97 |
+
"special": true
|
| 98 |
+
},
|
| 99 |
+
"92547": {
|
| 100 |
+
"content": "<quad>",
|
| 101 |
+
"lstrip": false,
|
| 102 |
+
"normalized": false,
|
| 103 |
+
"rstrip": false,
|
| 104 |
+
"single_word": false,
|
| 105 |
+
"special": true
|
| 106 |
+
},
|
| 107 |
+
"92548": {
|
| 108 |
+
"content": "</quad>",
|
| 109 |
+
"lstrip": false,
|
| 110 |
+
"normalized": false,
|
| 111 |
+
"rstrip": false,
|
| 112 |
+
"single_word": false,
|
| 113 |
+
"special": true
|
| 114 |
+
},
|
| 115 |
+
"92549": {
|
| 116 |
+
"content": "<ref>",
|
| 117 |
+
"lstrip": false,
|
| 118 |
+
"normalized": false,
|
| 119 |
+
"rstrip": false,
|
| 120 |
+
"single_word": false,
|
| 121 |
+
"special": true
|
| 122 |
+
},
|
| 123 |
+
"92550": {
|
| 124 |
+
"content": "</ref>",
|
| 125 |
+
"lstrip": false,
|
| 126 |
+
"normalized": false,
|
| 127 |
+
"rstrip": false,
|
| 128 |
+
"single_word": false,
|
| 129 |
+
"special": true
|
| 130 |
+
},
|
| 131 |
+
"92551": {
|
| 132 |
+
"content": "<box>",
|
| 133 |
+
"lstrip": false,
|
| 134 |
+
"normalized": false,
|
| 135 |
+
"rstrip": false,
|
| 136 |
+
"single_word": false,
|
| 137 |
+
"special": true
|
| 138 |
+
},
|
| 139 |
+
"92552": {
|
| 140 |
+
"content": "</box>",
|
| 141 |
+
"lstrip": false,
|
| 142 |
+
"normalized": false,
|
| 143 |
+
"rstrip": false,
|
| 144 |
+
"single_word": false,
|
| 145 |
+
"special": true
|
| 146 |
+
}
|
| 147 |
+
},
|
| 148 |
+
"additional_special_tokens": [
|
| 149 |
+
"<img>",
|
| 150 |
+
"</img>",
|
| 151 |
+
"<IMG_CONTEXT>",
|
| 152 |
+
"<quad>",
|
| 153 |
+
"</quad>",
|
| 154 |
+
"<ref>",
|
| 155 |
+
"</ref>",
|
| 156 |
+
"<box>",
|
| 157 |
+
"</box>"
|
| 158 |
+
],
|
| 159 |
+
"auto_map": {
|
| 160 |
+
"AutoTokenizer": [
|
| 161 |
+
"tokenization_internlm2.InternLM2Tokenizer",
|
| 162 |
+
null
|
| 163 |
+
]
|
| 164 |
+
},
|
| 165 |
+
"bos_token": "<s>",
|
| 166 |
+
"chat_template": "{{ bos_token }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
| 167 |
+
"clean_up_tokenization_spaces": false,
|
| 168 |
+
"eos_token": "</s>",
|
| 169 |
+
"model_max_length": 2048,
|
| 170 |
+
"pad_token": "</s>",
|
| 171 |
+
"tokenizer_class": "InternLM2Tokenizer",
|
| 172 |
+
"unk_token": "<unk>"
|
| 173 |
+
}
|