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Browse files- README.md +178 -0
- config.json +38 -0
- generation_config.json +6 -0
- output-00001-of-00006.safetensors +3 -0
- output-00002-of-00006.safetensors +3 -0
- output-00003-of-00006.safetensors +3 -0
- output-00004-of-00006.safetensors +3 -0
- output-00005-of-00006.safetensors +3 -0
- output-00006-of-00006.safetensors +3 -0
- pytorch_model.bin.index.json +810 -0
- special_tokens_map.json +4 -0
- tokenizer.json +0 -0
- tokenizer_config.json +2061 -0
README.md
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| 1 |
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---
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| 2 |
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license: llama3
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- nvidia
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- chatqa-1.5
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- chatqa
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- llama-3
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- pytorch
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---
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| 13 |
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## Model Details
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| 16 |
+
We introduce Llama3-ChatQA-1.5, which excels at conversational question answering (QA) and retrieval-augmented generation (RAG). Llama3-ChatQA-1.5 is developed using an improved training recipe from [ChatQA (1.0)](https://arxiv.org/abs/2401.10225), and it is built on top of [Llama-3 base model](https://huggingface.co/meta-llama/Meta-Llama-3-8B). Specifically, we incorporate more conversational QA data to enhance its tabular and arithmetic calculation capability. Llama3-ChatQA-1.5 has two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B. Both models were originally trained using [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), we converted the checkpoints to Hugging Face format.
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## Other Resources
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[Llama3-ChatQA-1.5-8B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B)   [Evaluation Data](https://huggingface.co/datasets/nvidia/ConvRAG-Bench)   [Training Data](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data)   [Retriever](https://huggingface.co/nvidia/dragon-multiturn-query-encoder)
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## Benchmark Results
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Results in ConvRAG Bench are as follows:
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| | ChatQA-1.0-7B | Command-R-Plus | Llama-3-instruct-70b | GPT-4-0613 | ChatQA-1.0-70B | ChatQA-1.5-8B | ChatQA-1.5-70B |
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| 25 |
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| -- |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
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| Doc2Dial | 37.88 | 33.51 | 37.88 | 34.16 | 38.9 | 39.33 | 41.26 |
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| QuAC | 29.69 | 34.16 | 36.96 | 40.29 | 41.82 | 39.73 | 38.82 |
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| QReCC | 46.97 | 49.77 | 51.34 | 52.01 | 48.05 | 49.03 | 51.40 |
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| CoQA | 76.61 | 69.71 | 76.98 | 77.42 | 78.57 | 76.46 | 78.44 |
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| DoQA | 41.57 | 40.67 | 41.24 | 43.39 | 51.94 | 49.6 | 50.67 |
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| ConvFinQA | 51.61 | 71.21 | 76.6 | 81.28 | 73.69 | 78.46 | 81.88 |
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| SQA | 61.87 | 74.07 | 69.61 | 79.21 | 69.14 | 73.28 | 83.82 |
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| TopioCQA | 45.45 | 53.77 | 49.72 | 45.09 | 50.98 | 49.96 | 55.63 |
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| HybriDial* | 54.51 | 46.7 | 48.59 | 49.81 | 56.44 | 65.76 | 68.27 |
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| INSCIT | 30.96 | 35.76 | 36.23 | 36.34 | 31.9 | 30.1 | 32.31 |
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| Average (all) | 47.71 | 50.93 | 52.52 | 53.90 | 54.14 | 55.17 | 58.25 |
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| Average (exclude HybriDial) | 46.96 | 51.40 | 52.95 | 54.35 | 53.89 | 53.99 | 57.14 |
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| 38 |
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| 39 |
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Note that ChatQA-1.5 is built based on Llama-3 base model, and ChatQA-1.0 is built based on Llama-2 base model. ChatQA-1.5 used some samples from the HybriDial training dataset. To ensure fair comparison, we also compare average scores excluding HybriDial. The data and evaluation scripts for ConvRAG can be found [here](https://huggingface.co/datasets/nvidia/ConvRAG-Bench).
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## Prompt Format
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<pre>
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System: {System}
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{Context}
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User: {Question}
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Assistant: {Response}
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User: {Question}
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Assistant:
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</pre>
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## How to use
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### take the whole document as context
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This can be applied to the scenario where the whole document can be fitted into the model, so that there is no need to run retrieval over the document.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "nvidia/Llama3-ChatQA-1.5-70B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
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messages = [
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{"role": "user", "content": "what is the percentage change of the net income from Q4 FY23 to Q4 FY24?"}
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]
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document = """NVIDIA (NASDAQ: NVDA) today reported revenue for the fourth quarter ended January 28, 2024, of $22.1 billion, up 22% from the previous quarter and up 265% from a year ago.\nFor the quarter, GAAP earnings per diluted share was $4.93, up 33% from the previous quarter and up 765% from a year ago. Non-GAAP earnings per diluted share was $5.16, up 28% from the previous quarter and up 486% from a year ago.\nQ4 Fiscal 2024 Summary\nGAAP\n| $ in millions, except earnings per share | Q4 FY24 | Q3 FY24 | Q4 FY23 | Q/Q | Y/Y |\n| Revenue | $22,103 | $18,120 | $6,051 | Up 22% | Up 265% |\n| Gross margin | 76.0% | 74.0% | 63.3% | Up 2.0 pts | Up 12.7 pts |\n| Operating expenses | $3,176 | $2,983 | $2,576 | Up 6% | Up 23% |\n| Operating income | $13,615 | $10,417 | $1,257 | Up 31% | Up 983% |\n| Net income | $12,285 | $9,243 | $1,414 | Up 33% | Up 769% |\n| Diluted earnings per share | $4.93 | $3.71 | $0.57 | Up 33% | Up 765% |"""
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def get_formatted_input(messages, context):
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system = "System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context."
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instruction = "Please give a full and complete answer for the question."
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for item in messages:
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if item['role'] == "user":
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## only apply this instruction for the first user turn
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item['content'] = instruction + " " + item['content']
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break
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conversation = '\n\n'.join(["User: " + item["content"] if item["role"] == "user" else "Assistant: " + item["content"] for item in messages]) + "\n\nAssistant:"
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formatted_input = system + "\n\n" + context + "\n\n" + conversation
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return formatted_input
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formatted_input = get_formatted_input(messages, document)
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tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device)
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators)
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response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
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print(tokenizer.decode(response, skip_special_tokens=True))
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```
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### run retrieval to get top-n chunks as context
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This can be applied to the scenario when the document is very long, so that it is necessary to run retrieval. Here, we use our [Dragon-multiturn](https://huggingface.co/nvidia/dragon-multiturn-query-encoder) retriever which can handle conversatinoal query. In addition, we provide a few [documents](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-70B/tree/main/docs) for users to play with.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
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import torch
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import json
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## load ChatQA-1.5 tokenizer and model
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model_id = "nvidia/Llama3-ChatQA-1.5-70B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
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## load retriever tokenizer and model
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retriever_tokenizer = AutoTokenizer.from_pretrained('nvidia/dragon-multiturn-query-encoder')
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query_encoder = AutoModel.from_pretrained('nvidia/dragon-multiturn-query-encoder')
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context_encoder = AutoModel.from_pretrained('nvidia/dragon-multiturn-context-encoder')
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## prepare documents, we take landrover car manual document that we provide as an example
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chunk_list = json.load(open("docs.json"))['landrover']
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messages = [
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{"role": "user", "content": "how to connect the bluetooth in the car?"}
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]
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### running retrieval
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## convert query into a format as follows:
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| 133 |
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## user: {user}\nagent: {agent}\nuser: {user}
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formatted_query_for_retriever = '\n'.join([turn['role'] + ": " + turn['content'] for turn in messages]).strip()
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query_input = retriever_tokenizer(formatted_query_for_retriever, return_tensors='pt')
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| 137 |
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ctx_input = retriever_tokenizer(chunk_list, padding=True, truncation=True, max_length=512, return_tensors='pt')
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| 138 |
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query_emb = query_encoder(**query_input).last_hidden_state[:, 0, :]
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| 139 |
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ctx_emb = context_encoder(**ctx_input).last_hidden_state[:, 0, :]
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| 140 |
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| 141 |
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## Compute similarity scores using dot product and rank the similarity
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| 142 |
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similarities = query_emb.matmul(ctx_emb.transpose(0, 1)) # (1, num_ctx)
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| 143 |
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ranked_results = torch.argsort(similarities, dim=-1, descending=True) # (1, num_ctx)
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| 144 |
+
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| 145 |
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## get top-n chunks (n=5)
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| 146 |
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retrieved_chunks = [chunk_list[idx] for idx in ranked_results.tolist()[0][:5]]
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context = "\n\n".join(retrieved_chunks)
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| 148 |
+
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| 149 |
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### running text generation
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| 150 |
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formatted_input = get_formatted_input(messages, context)
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| 151 |
+
tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device)
|
| 152 |
+
|
| 153 |
+
terminators = [
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| 154 |
+
tokenizer.eos_token_id,
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| 155 |
+
tokenizer.convert_tokens_to_ids("<|eot_id|>")
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| 156 |
+
]
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| 157 |
+
outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators)
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| 158 |
+
|
| 159 |
+
response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
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| 160 |
+
print(tokenizer.decode(response, skip_special_tokens=True))
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+
```
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| 162 |
+
|
| 163 |
+
## Correspondence to
|
| 164 |
+
Zihan Liu ([email protected]), Wei Ping ([email protected])
|
| 165 |
+
|
| 166 |
+
## Citation
|
| 167 |
+
<pre>
|
| 168 |
+
@article{liu2024chatqa,
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| 169 |
+
title={ChatQA: Building GPT-4 Level Conversational QA Models},
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| 170 |
+
author={Liu, Zihan and Ping, Wei and Roy, Rajarshi and Xu, Peng and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan},
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| 171 |
+
journal={arXiv preprint arXiv:2401.10225},
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| 172 |
+
year={2024}}
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| 173 |
+
</pre>
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+
|
| 175 |
+
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| 176 |
+
## License
|
| 177 |
+
The use of this model is governed by the [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/)
|
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+
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config.json
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{
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"architectures": [
|
| 3 |
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"LlamaForCausalLM"
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| 4 |
+
],
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| 5 |
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"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
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| 7 |
+
"bos_token_id": 128000,
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| 8 |
+
"eos_token_id": 128001,
|
| 9 |
+
"hidden_act": "silu",
|
| 10 |
+
"hidden_size": 8192,
|
| 11 |
+
"initializer_range": 0.02,
|
| 12 |
+
"intermediate_size": 28672,
|
| 13 |
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"max_position_embeddings": 8192,
|
| 14 |
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"model_type": "llama",
|
| 15 |
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"num_attention_heads": 64,
|
| 16 |
+
"num_hidden_layers": 80,
|
| 17 |
+
"num_key_value_heads": 8,
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| 18 |
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"pretraining_tp": 1,
|
| 19 |
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"rms_norm_eps": 1e-05,
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| 20 |
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"rope_scaling": null,
|
| 21 |
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"rope_theta": 500000.0,
|
| 22 |
+
"tie_word_embeddings": false,
|
| 23 |
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"torch_dtype": "float16",
|
| 24 |
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"transformers_version": "4.40.0.dev0",
|
| 25 |
+
"use_cache": true,
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| 26 |
+
"vocab_size": 128256,
|
| 27 |
+
"quantization_config": {
|
| 28 |
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"quant_method": "exl2",
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| 29 |
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"version": "0.0.20",
|
| 30 |
+
"bits": 5.0,
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| 31 |
+
"head_bits": 6,
|
| 32 |
+
"calibration": {
|
| 33 |
+
"rows": 100,
|
| 34 |
+
"length": 2048,
|
| 35 |
+
"dataset": "(default)"
|
| 36 |
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}
|
| 37 |
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|
| 38 |
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|
generation_config.json
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|
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|
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|
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|
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|
| 6 |
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|
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|
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|
| 809 |
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|
| 810 |
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}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,4 @@
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{
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| 2 |
+
"bos_token": "<|begin_of_text|>",
|
| 3 |
+
"eos_token": "<|end_of_text|>"
|
| 4 |
+
}
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"128000": {
|
| 4 |
+
"content": "<|begin_of_text|>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"128001": {
|
| 12 |
+
"content": "<|end_of_text|>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"128002": {
|
| 20 |
+
"content": "<|reserved_special_token_0|>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"128003": {
|
| 28 |
+
"content": "<|reserved_special_token_1|>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"128004": {
|
| 36 |
+
"content": "<|reserved_special_token_2|>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
},
|
| 43 |
+
"128005": {
|
| 44 |
+
"content": "<|reserved_special_token_3|>",
|
| 45 |
+
"lstrip": false,
|
| 46 |
+
"normalized": false,
|
| 47 |
+
"rstrip": false,
|
| 48 |
+
"single_word": false,
|
| 49 |
+
"special": true
|
| 50 |
+
},
|
| 51 |
+
"128006": {
|
| 52 |
+
"content": "<|start_header_id|>",
|
| 53 |
+
"lstrip": false,
|
| 54 |
+
"normalized": false,
|
| 55 |
+
"rstrip": false,
|
| 56 |
+
"single_word": false,
|
| 57 |
+
"special": true
|
| 58 |
+
},
|
| 59 |
+
"128007": {
|
| 60 |
+
"content": "<|end_header_id|>",
|
| 61 |
+
"lstrip": false,
|
| 62 |
+
"normalized": false,
|
| 63 |
+
"rstrip": false,
|
| 64 |
+
"single_word": false,
|
| 65 |
+
"special": true
|
| 66 |
+
},
|
| 67 |
+
"128008": {
|
| 68 |
+
"content": "<|reserved_special_token_4|>",
|
| 69 |
+
"lstrip": false,
|
| 70 |
+
"normalized": false,
|
| 71 |
+
"rstrip": false,
|
| 72 |
+
"single_word": false,
|
| 73 |
+
"special": true
|
| 74 |
+
},
|
| 75 |
+
"128009": {
|
| 76 |
+
"content": "<|eot_id|>",
|
| 77 |
+
"lstrip": false,
|
| 78 |
+
"normalized": false,
|
| 79 |
+
"rstrip": false,
|
| 80 |
+
"single_word": false,
|
| 81 |
+
"special": true
|
| 82 |
+
},
|
| 83 |
+
"128010": {
|
| 84 |
+
"content": "<|reserved_special_token_5|>",
|
| 85 |
+
"lstrip": false,
|
| 86 |
+
"normalized": false,
|
| 87 |
+
"rstrip": false,
|
| 88 |
+
"single_word": false,
|
| 89 |
+
"special": true
|
| 90 |
+
},
|
| 91 |
+
"128011": {
|
| 92 |
+
"content": "<|reserved_special_token_6|>",
|
| 93 |
+
"lstrip": false,
|
| 94 |
+
"normalized": false,
|
| 95 |
+
"rstrip": false,
|
| 96 |
+
"single_word": false,
|
| 97 |
+
"special": true
|
| 98 |
+
},
|
| 99 |
+
"128012": {
|
| 100 |
+
"content": "<|reserved_special_token_7|>",
|
| 101 |
+
"lstrip": false,
|
| 102 |
+
"normalized": false,
|
| 103 |
+
"rstrip": false,
|
| 104 |
+
"single_word": false,
|
| 105 |
+
"special": true
|
| 106 |
+
},
|
| 107 |
+
"128013": {
|
| 108 |
+
"content": "<|reserved_special_token_8|>",
|
| 109 |
+
"lstrip": false,
|
| 110 |
+
"normalized": false,
|
| 111 |
+
"rstrip": false,
|
| 112 |
+
"single_word": false,
|
| 113 |
+
"special": true
|
| 114 |
+
},
|
| 115 |
+
"128014": {
|
| 116 |
+
"content": "<|reserved_special_token_9|>",
|
| 117 |
+
"lstrip": false,
|
| 118 |
+
"normalized": false,
|
| 119 |
+
"rstrip": false,
|
| 120 |
+
"single_word": false,
|
| 121 |
+
"special": true
|
| 122 |
+
},
|
| 123 |
+
"128015": {
|
| 124 |
+
"content": "<|reserved_special_token_10|>",
|
| 125 |
+
"lstrip": false,
|
| 126 |
+
"normalized": false,
|
| 127 |
+
"rstrip": false,
|
| 128 |
+
"single_word": false,
|
| 129 |
+
"special": true
|
| 130 |
+
},
|
| 131 |
+
"128016": {
|
| 132 |
+
"content": "<|reserved_special_token_11|>",
|
| 133 |
+
"lstrip": false,
|
| 134 |
+
"normalized": false,
|
| 135 |
+
"rstrip": false,
|
| 136 |
+
"single_word": false,
|
| 137 |
+
"special": true
|
| 138 |
+
},
|
| 139 |
+
"128017": {
|
| 140 |
+
"content": "<|reserved_special_token_12|>",
|
| 141 |
+
"lstrip": false,
|
| 142 |
+
"normalized": false,
|
| 143 |
+
"rstrip": false,
|
| 144 |
+
"single_word": false,
|
| 145 |
+
"special": true
|
| 146 |
+
},
|
| 147 |
+
"128018": {
|
| 148 |
+
"content": "<|reserved_special_token_13|>",
|
| 149 |
+
"lstrip": false,
|
| 150 |
+
"normalized": false,
|
| 151 |
+
"rstrip": false,
|
| 152 |
+
"single_word": false,
|
| 153 |
+
"special": true
|
| 154 |
+
},
|
| 155 |
+
"128019": {
|
| 156 |
+
"content": "<|reserved_special_token_14|>",
|
| 157 |
+
"lstrip": false,
|
| 158 |
+
"normalized": false,
|
| 159 |
+
"rstrip": false,
|
| 160 |
+
"single_word": false,
|
| 161 |
+
"special": true
|
| 162 |
+
},
|
| 163 |
+
"128020": {
|
| 164 |
+
"content": "<|reserved_special_token_15|>",
|
| 165 |
+
"lstrip": false,
|
| 166 |
+
"normalized": false,
|
| 167 |
+
"rstrip": false,
|
| 168 |
+
"single_word": false,
|
| 169 |
+
"special": true
|
| 170 |
+
},
|
| 171 |
+
"128021": {
|
| 172 |
+
"content": "<|reserved_special_token_16|>",
|
| 173 |
+
"lstrip": false,
|
| 174 |
+
"normalized": false,
|
| 175 |
+
"rstrip": false,
|
| 176 |
+
"single_word": false,
|
| 177 |
+
"special": true
|
| 178 |
+
},
|
| 179 |
+
"128022": {
|
| 180 |
+
"content": "<|reserved_special_token_17|>",
|
| 181 |
+
"lstrip": false,
|
| 182 |
+
"normalized": false,
|
| 183 |
+
"rstrip": false,
|
| 184 |
+
"single_word": false,
|
| 185 |
+
"special": true
|
| 186 |
+
},
|
| 187 |
+
"128023": {
|
| 188 |
+
"content": "<|reserved_special_token_18|>",
|
| 189 |
+
"lstrip": false,
|
| 190 |
+
"normalized": false,
|
| 191 |
+
"rstrip": false,
|
| 192 |
+
"single_word": false,
|
| 193 |
+
"special": true
|
| 194 |
+
},
|
| 195 |
+
"128024": {
|
| 196 |
+
"content": "<|reserved_special_token_19|>",
|
| 197 |
+
"lstrip": false,
|
| 198 |
+
"normalized": false,
|
| 199 |
+
"rstrip": false,
|
| 200 |
+
"single_word": false,
|
| 201 |
+
"special": true
|
| 202 |
+
},
|
| 203 |
+
"128025": {
|
| 204 |
+
"content": "<|reserved_special_token_20|>",
|
| 205 |
+
"lstrip": false,
|
| 206 |
+
"normalized": false,
|
| 207 |
+
"rstrip": false,
|
| 208 |
+
"single_word": false,
|
| 209 |
+
"special": true
|
| 210 |
+
},
|
| 211 |
+
"128026": {
|
| 212 |
+
"content": "<|reserved_special_token_21|>",
|
| 213 |
+
"lstrip": false,
|
| 214 |
+
"normalized": false,
|
| 215 |
+
"rstrip": false,
|
| 216 |
+
"single_word": false,
|
| 217 |
+
"special": true
|
| 218 |
+
},
|
| 219 |
+
"128027": {
|
| 220 |
+
"content": "<|reserved_special_token_22|>",
|
| 221 |
+
"lstrip": false,
|
| 222 |
+
"normalized": false,
|
| 223 |
+
"rstrip": false,
|
| 224 |
+
"single_word": false,
|
| 225 |
+
"special": true
|
| 226 |
+
},
|
| 227 |
+
"128028": {
|
| 228 |
+
"content": "<|reserved_special_token_23|>",
|
| 229 |
+
"lstrip": false,
|
| 230 |
+
"normalized": false,
|
| 231 |
+
"rstrip": false,
|
| 232 |
+
"single_word": false,
|
| 233 |
+
"special": true
|
| 234 |
+
},
|
| 235 |
+
"128029": {
|
| 236 |
+
"content": "<|reserved_special_token_24|>",
|
| 237 |
+
"lstrip": false,
|
| 238 |
+
"normalized": false,
|
| 239 |
+
"rstrip": false,
|
| 240 |
+
"single_word": false,
|
| 241 |
+
"special": true
|
| 242 |
+
},
|
| 243 |
+
"128030": {
|
| 244 |
+
"content": "<|reserved_special_token_25|>",
|
| 245 |
+
"lstrip": false,
|
| 246 |
+
"normalized": false,
|
| 247 |
+
"rstrip": false,
|
| 248 |
+
"single_word": false,
|
| 249 |
+
"special": true
|
| 250 |
+
},
|
| 251 |
+
"128031": {
|
| 252 |
+
"content": "<|reserved_special_token_26|>",
|
| 253 |
+
"lstrip": false,
|
| 254 |
+
"normalized": false,
|
| 255 |
+
"rstrip": false,
|
| 256 |
+
"single_word": false,
|
| 257 |
+
"special": true
|
| 258 |
+
},
|
| 259 |
+
"128032": {
|
| 260 |
+
"content": "<|reserved_special_token_27|>",
|
| 261 |
+
"lstrip": false,
|
| 262 |
+
"normalized": false,
|
| 263 |
+
"rstrip": false,
|
| 264 |
+
"single_word": false,
|
| 265 |
+
"special": true
|
| 266 |
+
},
|
| 267 |
+
"128033": {
|
| 268 |
+
"content": "<|reserved_special_token_28|>",
|
| 269 |
+
"lstrip": false,
|
| 270 |
+
"normalized": false,
|
| 271 |
+
"rstrip": false,
|
| 272 |
+
"single_word": false,
|
| 273 |
+
"special": true
|
| 274 |
+
},
|
| 275 |
+
"128034": {
|
| 276 |
+
"content": "<|reserved_special_token_29|>",
|
| 277 |
+
"lstrip": false,
|
| 278 |
+
"normalized": false,
|
| 279 |
+
"rstrip": false,
|
| 280 |
+
"single_word": false,
|
| 281 |
+
"special": true
|
| 282 |
+
},
|
| 283 |
+
"128035": {
|
| 284 |
+
"content": "<|reserved_special_token_30|>",
|
| 285 |
+
"lstrip": false,
|
| 286 |
+
"normalized": false,
|
| 287 |
+
"rstrip": false,
|
| 288 |
+
"single_word": false,
|
| 289 |
+
"special": true
|
| 290 |
+
},
|
| 291 |
+
"128036": {
|
| 292 |
+
"content": "<|reserved_special_token_31|>",
|
| 293 |
+
"lstrip": false,
|
| 294 |
+
"normalized": false,
|
| 295 |
+
"rstrip": false,
|
| 296 |
+
"single_word": false,
|
| 297 |
+
"special": true
|
| 298 |
+
},
|
| 299 |
+
"128037": {
|
| 300 |
+
"content": "<|reserved_special_token_32|>",
|
| 301 |
+
"lstrip": false,
|
| 302 |
+
"normalized": false,
|
| 303 |
+
"rstrip": false,
|
| 304 |
+
"single_word": false,
|
| 305 |
+
"special": true
|
| 306 |
+
},
|
| 307 |
+
"128038": {
|
| 308 |
+
"content": "<|reserved_special_token_33|>",
|
| 309 |
+
"lstrip": false,
|
| 310 |
+
"normalized": false,
|
| 311 |
+
"rstrip": false,
|
| 312 |
+
"single_word": false,
|
| 313 |
+
"special": true
|
| 314 |
+
},
|
| 315 |
+
"128039": {
|
| 316 |
+
"content": "<|reserved_special_token_34|>",
|
| 317 |
+
"lstrip": false,
|
| 318 |
+
"normalized": false,
|
| 319 |
+
"rstrip": false,
|
| 320 |
+
"single_word": false,
|
| 321 |
+
"special": true
|
| 322 |
+
},
|
| 323 |
+
"128040": {
|
| 324 |
+
"content": "<|reserved_special_token_35|>",
|
| 325 |
+
"lstrip": false,
|
| 326 |
+
"normalized": false,
|
| 327 |
+
"rstrip": false,
|
| 328 |
+
"single_word": false,
|
| 329 |
+
"special": true
|
| 330 |
+
},
|
| 331 |
+
"128041": {
|
| 332 |
+
"content": "<|reserved_special_token_36|>",
|
| 333 |
+
"lstrip": false,
|
| 334 |
+
"normalized": false,
|
| 335 |
+
"rstrip": false,
|
| 336 |
+
"single_word": false,
|
| 337 |
+
"special": true
|
| 338 |
+
},
|
| 339 |
+
"128042": {
|
| 340 |
+
"content": "<|reserved_special_token_37|>",
|
| 341 |
+
"lstrip": false,
|
| 342 |
+
"normalized": false,
|
| 343 |
+
"rstrip": false,
|
| 344 |
+
"single_word": false,
|
| 345 |
+
"special": true
|
| 346 |
+
},
|
| 347 |
+
"128043": {
|
| 348 |
+
"content": "<|reserved_special_token_38|>",
|
| 349 |
+
"lstrip": false,
|
| 350 |
+
"normalized": false,
|
| 351 |
+
"rstrip": false,
|
| 352 |
+
"single_word": false,
|
| 353 |
+
"special": true
|
| 354 |
+
},
|
| 355 |
+
"128044": {
|
| 356 |
+
"content": "<|reserved_special_token_39|>",
|
| 357 |
+
"lstrip": false,
|
| 358 |
+
"normalized": false,
|
| 359 |
+
"rstrip": false,
|
| 360 |
+
"single_word": false,
|
| 361 |
+
"special": true
|
| 362 |
+
},
|
| 363 |
+
"128045": {
|
| 364 |
+
"content": "<|reserved_special_token_40|>",
|
| 365 |
+
"lstrip": false,
|
| 366 |
+
"normalized": false,
|
| 367 |
+
"rstrip": false,
|
| 368 |
+
"single_word": false,
|
| 369 |
+
"special": true
|
| 370 |
+
},
|
| 371 |
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|
| 1602 |
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|
| 1603 |
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|
| 1604 |
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|
| 1605 |
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| 1606 |
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| 1607 |
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| 1608 |
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| 1609 |
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|
| 1610 |
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| 1611 |
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|
| 1612 |
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|
| 1613 |
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| 1614 |
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| 1616 |
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| 1617 |
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|
| 1618 |
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| 1619 |
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|
| 1620 |
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|
| 1621 |
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| 1622 |
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| 1624 |
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| 1625 |
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|
| 1626 |
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| 1627 |
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|
| 1628 |
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|
| 1629 |
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| 1630 |
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| 1632 |
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| 1633 |
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|
| 1634 |
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| 1635 |
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|
| 1636 |
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| 1637 |
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| 1638 |
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| 1640 |
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| 1641 |
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| 1642 |
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| 1643 |
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| 1644 |
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| 1645 |
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| 1646 |
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| 1649 |
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| 1650 |
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|
| 1652 |
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| 1653 |
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| 1657 |
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| 1658 |
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| 1660 |
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| 1661 |
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| 1665 |
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| 1666 |
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| 1668 |
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| 1669 |
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| 1673 |
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| 1674 |
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| 1676 |
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| 1677 |
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| 1684 |
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| 1689 |
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| 1690 |
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| 1692 |
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| 1693 |
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| 1697 |
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| 1698 |
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| 1700 |
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| 1708 |
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| 1716 |
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| 1724 |
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| 1725 |
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| 1732 |
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| 1740 |
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| 1741 |
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| 1746 |
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| 1748 |
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| 1749 |
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| 1754 |
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| 1756 |
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| 1757 |
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| 1762 |
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| 1764 |
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| 1765 |
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| 1769 |
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| 1770 |
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| 1772 |
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| 1773 |
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| 1777 |
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| 1778 |
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| 1779 |
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|
| 1780 |
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| 1781 |
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| 1785 |
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|
| 1786 |
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| 1787 |
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|
| 1788 |
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| 1789 |
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| 1790 |
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| 1793 |
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|
| 1794 |
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| 1795 |
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| 1796 |
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| 1797 |
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| 1798 |
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| 1800 |
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| 1801 |
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|
| 1802 |
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|
| 1803 |
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|
| 1804 |
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|
| 1805 |
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| 1809 |
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|
| 1810 |
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| 1811 |
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|
| 1812 |
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|
| 1813 |
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| 1814 |
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| 1816 |
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| 1817 |
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|
| 1818 |
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|
| 1819 |
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|
| 1820 |
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|
| 1821 |
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| 1822 |
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| 1823 |
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| 1824 |
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|
| 1825 |
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|
| 1826 |
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|
| 1827 |
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|
| 1828 |
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|
| 1829 |
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|
| 1830 |
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|
| 1831 |
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|
| 1832 |
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|
| 1833 |
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|
| 1834 |
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|
| 1835 |
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|
| 1836 |
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|
| 1837 |
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|
| 1838 |
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|
| 1839 |
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|
| 1840 |
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|
| 1841 |
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|
| 1842 |
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|
| 1843 |
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|
| 1844 |
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|
| 1845 |
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|
| 1846 |
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|
| 1847 |
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|
| 1848 |
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|
| 1849 |
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|
| 1850 |
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|
| 1851 |
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|
| 1852 |
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|
| 1853 |
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|
| 1854 |
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|
| 1855 |
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|
| 1856 |
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|
| 1857 |
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|
| 1858 |
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|
| 1859 |
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|
| 1860 |
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|
| 1861 |
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|
| 1862 |
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|
| 1863 |
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|
| 1864 |
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|
| 1865 |
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|
| 1866 |
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|
| 1867 |
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|
| 1868 |
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|
| 1869 |
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|
| 1870 |
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|
| 1871 |
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|
| 1872 |
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|
| 1873 |
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|
| 1874 |
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|
| 1875 |
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|
| 1876 |
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|
| 1877 |
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|
| 1878 |
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|
| 1879 |
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|
| 1880 |
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|
| 1881 |
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|
| 1882 |
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},
|
| 1883 |
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|
| 1884 |
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"content": "<|reserved_special_token_230|>",
|
| 1885 |
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|
| 1886 |
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|
| 1887 |
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|
| 1888 |
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|
| 1889 |
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|
| 1890 |
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|
| 1891 |
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|
| 1892 |
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|
| 1893 |
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|
| 1894 |
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|
| 1895 |
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|
| 1896 |
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|
| 1897 |
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|
| 1898 |
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},
|
| 1899 |
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|
| 1900 |
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|
| 1901 |
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|
| 1902 |
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|
| 1903 |
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|
| 1904 |
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|
| 1905 |
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|
| 1906 |
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|
| 1907 |
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|
| 1908 |
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"content": "<|reserved_special_token_233|>",
|
| 1909 |
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|
| 1910 |
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|
| 1911 |
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|
| 1912 |
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|
| 1913 |
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|
| 1914 |
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},
|
| 1915 |
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|
| 1916 |
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"content": "<|reserved_special_token_234|>",
|
| 1917 |
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|
| 1918 |
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|
| 1919 |
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|
| 1920 |
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|
| 1921 |
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|
| 1922 |
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},
|
| 1923 |
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|
| 1924 |
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"content": "<|reserved_special_token_235|>",
|
| 1925 |
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|
| 1926 |
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|
| 1927 |
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|
| 1928 |
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|
| 1929 |
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|
| 1930 |
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},
|
| 1931 |
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|
| 1932 |
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"content": "<|reserved_special_token_236|>",
|
| 1933 |
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|
| 1934 |
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|
| 1935 |
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|
| 1936 |
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|
| 1937 |
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|
| 1938 |
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},
|
| 1939 |
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|
| 1940 |
+
"content": "<|reserved_special_token_237|>",
|
| 1941 |
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|
| 1942 |
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|
| 1943 |
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|
| 1944 |
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|
| 1945 |
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|
| 1946 |
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},
|
| 1947 |
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|
| 1948 |
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"content": "<|reserved_special_token_238|>",
|
| 1949 |
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|
| 1950 |
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|
| 1951 |
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|
| 1952 |
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|
| 1953 |
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|
| 1954 |
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},
|
| 1955 |
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|
| 1956 |
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"content": "<|reserved_special_token_239|>",
|
| 1957 |
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|
| 1958 |
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|
| 1959 |
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|
| 1960 |
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|
| 1961 |
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|
| 1962 |
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},
|
| 1963 |
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"128245": {
|
| 1964 |
+
"content": "<|reserved_special_token_240|>",
|
| 1965 |
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|
| 1966 |
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|
| 1967 |
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|
| 1968 |
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|
| 1969 |
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|
| 1970 |
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},
|
| 1971 |
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"128246": {
|
| 1972 |
+
"content": "<|reserved_special_token_241|>",
|
| 1973 |
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|
| 1974 |
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|
| 1975 |
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|
| 1976 |
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|
| 1977 |
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|
| 1978 |
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},
|
| 1979 |
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"128247": {
|
| 1980 |
+
"content": "<|reserved_special_token_242|>",
|
| 1981 |
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|
| 1982 |
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|
| 1983 |
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|
| 1984 |
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|
| 1985 |
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|
| 1986 |
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},
|
| 1987 |
+
"128248": {
|
| 1988 |
+
"content": "<|reserved_special_token_243|>",
|
| 1989 |
+
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|
| 1990 |
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|
| 1991 |
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|
| 1992 |
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|
| 1993 |
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|
| 1994 |
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},
|
| 1995 |
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"128249": {
|
| 1996 |
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"content": "<|reserved_special_token_244|>",
|
| 1997 |
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|
| 1998 |
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|
| 1999 |
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|
| 2000 |
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|
| 2001 |
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|
| 2002 |
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},
|
| 2003 |
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"128250": {
|
| 2004 |
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"content": "<|reserved_special_token_245|>",
|
| 2005 |
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|
| 2006 |
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|
| 2007 |
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|
| 2008 |
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|
| 2009 |
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|
| 2010 |
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},
|
| 2011 |
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"128251": {
|
| 2012 |
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"content": "<|reserved_special_token_246|>",
|
| 2013 |
+
"lstrip": false,
|
| 2014 |
+
"normalized": false,
|
| 2015 |
+
"rstrip": false,
|
| 2016 |
+
"single_word": false,
|
| 2017 |
+
"special": true
|
| 2018 |
+
},
|
| 2019 |
+
"128252": {
|
| 2020 |
+
"content": "<|reserved_special_token_247|>",
|
| 2021 |
+
"lstrip": false,
|
| 2022 |
+
"normalized": false,
|
| 2023 |
+
"rstrip": false,
|
| 2024 |
+
"single_word": false,
|
| 2025 |
+
"special": true
|
| 2026 |
+
},
|
| 2027 |
+
"128253": {
|
| 2028 |
+
"content": "<|reserved_special_token_248|>",
|
| 2029 |
+
"lstrip": false,
|
| 2030 |
+
"normalized": false,
|
| 2031 |
+
"rstrip": false,
|
| 2032 |
+
"single_word": false,
|
| 2033 |
+
"special": true
|
| 2034 |
+
},
|
| 2035 |
+
"128254": {
|
| 2036 |
+
"content": "<|reserved_special_token_249|>",
|
| 2037 |
+
"lstrip": false,
|
| 2038 |
+
"normalized": false,
|
| 2039 |
+
"rstrip": false,
|
| 2040 |
+
"single_word": false,
|
| 2041 |
+
"special": true
|
| 2042 |
+
},
|
| 2043 |
+
"128255": {
|
| 2044 |
+
"content": "<|reserved_special_token_250|>",
|
| 2045 |
+
"lstrip": false,
|
| 2046 |
+
"normalized": false,
|
| 2047 |
+
"rstrip": false,
|
| 2048 |
+
"single_word": false,
|
| 2049 |
+
"special": true
|
| 2050 |
+
}
|
| 2051 |
+
},
|
| 2052 |
+
"bos_token": "<|begin_of_text|>",
|
| 2053 |
+
"clean_up_tokenization_spaces": true,
|
| 2054 |
+
"eos_token": "<|end_of_text|>",
|
| 2055 |
+
"model_input_names": [
|
| 2056 |
+
"input_ids",
|
| 2057 |
+
"attention_mask"
|
| 2058 |
+
],
|
| 2059 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 2060 |
+
"tokenizer_class": "PreTrainedTokenizerFast"
|
| 2061 |
+
}
|