Upload folder using huggingface_hub
Browse files- README.md +133 -0
- config.json +10 -0
- generation_config.json +7 -0
- model.bin +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer_config.json +2108 -0
- vocabulary.json +0 -0
README.md
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---
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license: apache-2.0
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language:
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- en
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- de
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- es
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- fr
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- it
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- pt
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- pl
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- nl
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- tr
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- sv
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- cs
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- el
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- hu
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- ro
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- fi
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- uk
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- sl
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- sk
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- da
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- lt
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- lv
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- et
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- bg
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- 'no'
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- ca
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- hr
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- ga
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- mt
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- gl
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- zh
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- ru
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- ko
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- ja
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- ar
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- hi
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library_name: transformers
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base_model:
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- utter-project/EuroLLM-9B
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---
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# Model Card for EuroLLM-9B-Instruct
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This is the model card for EuroLLM-9B-Instruct. You can also check the pre-trained version: [EuroLLM-9B](https://huggingface.co/utter-project/EuroLLM-9B).
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- **Developed by:** Unbabel, Instituto Superior Técnico, Instituto de Telecomunicações, University of Edinburgh, Aveni, University of Paris-Saclay, University of Amsterdam, Naver Labs, Sorbonne Université.
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- **Funded by:** European Union.
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- **Model type:** A 9B parameter multilingual transfomer LLM.
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- **Language(s) (NLP):** Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Arabic, Catalan, Chinese, Galician, Hindi, Japanese, Korean, Norwegian, Russian, Turkish, and Ukrainian.
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- **License:** Apache License 2.0.
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## Model Details
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The EuroLLM project has the goal of creating a suite of LLMs capable of understanding and generating text in all European Union languages as well as some additional relevant languages.
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EuroLLM-9B is a 9B parameter model trained on 4 trillion tokens divided across the considered languages and several data sources: Web data, parallel data (en-xx and xx-en), and high-quality datasets.
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EuroLLM-9B-Instruct was further instruction tuned on EuroBlocks, an instruction tuning dataset with focus on general instruction-following and machine translation.
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### Model Description
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EuroLLM uses a standard, dense Transformer architecture:
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- We use grouped query attention (GQA) with 8 key-value heads, since it has been shown to increase speed at inference time while maintaining downstream performance.
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- We perform pre-layer normalization, since it improves the training stability, and use the RMSNorm, which is faster.
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- We use the SwiGLU activation function, since it has been shown to lead to good results on downstream tasks.
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- We use rotary positional embeddings (RoPE) in every layer, since these have been shown to lead to good performances while allowing the extension of the context length.
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For pre-training, we use 400 Nvidia H100 GPUs of the Marenostrum 5 supercomputer, training the model with a constant batch size of 2,800 sequences, which corresponds to approximately 12 million tokens, using the Adam optimizer, and BF16 precision.
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Here is a summary of the model hyper-parameters:
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| | |
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|--------------------------------------|----------------------|
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| Sequence Length | 4,096 |
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| Number of Layers | 42 |
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| 75 |
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| Embedding Size | 4,096 |
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| FFN Hidden Size | 12,288 |
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| Number of Heads | 32 |
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| Number of KV Heads (GQA) | 8 |
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| Activation Function | SwiGLU |
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| Position Encodings | RoPE (\Theta=10,000) |
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| Layer Norm | RMSNorm |
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| Tied Embeddings | No |
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| Embedding Parameters | 0.524B |
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| LM Head Parameters | 0.524B |
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| Non-embedding Parameters | 8.105B |
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| Total Parameters | 9.154B |
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## Run the model
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "utter-project/EuroLLM-9B-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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messages = [
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{
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"role": "system",
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"content": "You are EuroLLM --- an AI assistant specialized in European languages that provides safe, educational and helpful answers.",
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},
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{
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"role": "user", "content": "What is the capital of Portugal? How would you describe it?"
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},
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]
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inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
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outputs = model.generate(inputs, max_new_tokens=1024)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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## Results
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### EU Languages
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**Table 1:** Comparison of open-weight LLMs on multilingual benchmarks. The borda count corresponds to the average ranking of the models (see ([Colombo et al., 2022](https://arxiv.org/abs/2202.03799))). For Arc-challenge, Hellaswag, and MMLU we are using Okapi datasets ([Lai et al., 2023](https://aclanthology.org/2023.emnlp-demo.28/)) which include 11 languages. For MMLU-Pro and MUSR we translate the English version with Tower ([Alves et al., 2024](https://arxiv.org/abs/2402.17733)) to 6 EU languages.
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\* As there are no public versions of the pre-trained models, we evaluated them using the post-trained versions.
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The results in Table 1 highlight EuroLLM-9B's superior performance on multilingual tasks compared to other European-developed models (as shown by the Borda count of 1.0), as well as its strong competitiveness with non-European models, achieving results comparable to Gemma-2-9B and outperforming the rest on most benchmarks.
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### English
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**Table 2:** Comparison of open-weight LLMs on English general benchmarks.
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\* As there are no public versions of the pre-trained models, we evaluated them using the post-trained versions.
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The results in Table 2 demonstrate EuroLLM's strong performance on English tasks, surpassing most European-developed models and matching the performance of Mistral-7B (obtaining the same Borda count).
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## Bias, Risks, and Limitations
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EuroLLM-9B has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
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config.json
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{
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"bos_token": "<s>",
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"eos_token": "<|im_end|>",
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"layer_norm_epsilon": 1e-05,
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"multi_query_attention": true,
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| 6 |
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"quantization_bits": null,
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| 7 |
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"quantization_group_size": null,
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"quantization_type": 0,
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"unk_token": "<unk>"
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 3,
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"do_sample": true,
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"eos_token_id": 4,
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"transformers_version": "4.44.2"
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}
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model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:9a0126d17a10ef08c71e5b1995bf3156156a60dcf1529884ebe199c1189f4d75
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size 9160268507
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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| 27 |
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer_config.json
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|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"add_prefix_space": false,
|
| 5 |
+
"added_tokens_decoder": {
|
| 6 |
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"0": {
|
| 7 |
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"content": "<unk>",
|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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"special": true
|
| 13 |
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},
|
| 14 |
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"1": {
|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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},
|
| 22 |
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"2": {
|
| 23 |
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"content": "</s>",
|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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"3": {
|
| 31 |
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"content": "<|im_start|>",
|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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},
|
| 38 |
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"4": {
|
| 39 |
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|
| 40 |
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|
| 41 |
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| 42 |
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| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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| 50 |
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| 51 |
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| 52 |
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|
| 53 |
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| 54 |
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|
| 55 |
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| 56 |
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| 57 |
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| 58 |
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| 59 |
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|
| 60 |
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|
| 61 |
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| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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| 81 |
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| 82 |
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| 83 |
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|
| 84 |
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|
| 85 |
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| 86 |
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|
| 87 |
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|
| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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|
| 93 |
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| 94 |
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|
| 95 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 100 |
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|
| 101 |
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| 102 |
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|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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| 107 |
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|
| 108 |
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|
| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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| 114 |
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| 115 |
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| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 120 |
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| 121 |
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| 122 |
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| 123 |
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| 124 |
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| 125 |
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| 126 |
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| 127 |
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| 128 |
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| 129 |
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| 130 |
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| 131 |
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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| 136 |
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| 137 |
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| 138 |
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| 139 |
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| 140 |
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| 141 |
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| 142 |
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| 143 |
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| 144 |
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| 145 |
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| 146 |
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| 147 |
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| 148 |
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| 149 |
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| 150 |
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| 151 |
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| 152 |
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| 153 |
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| 154 |
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| 155 |
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| 156 |
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| 157 |
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| 158 |
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| 159 |
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| 160 |
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| 161 |
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| 162 |
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| 163 |
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| 164 |
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| 165 |
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| 166 |
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| 167 |
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| 168 |
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| 169 |
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| 170 |
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| 171 |
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| 172 |
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| 173 |
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| 174 |
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| 175 |
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| 176 |
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| 177 |
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| 178 |
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| 179 |
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| 180 |
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| 181 |
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| 182 |
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| 183 |
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| 184 |
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| 185 |
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| 186 |
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| 187 |
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| 188 |
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| 189 |
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| 190 |
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| 191 |
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|
| 192 |
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|
| 193 |
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|
| 194 |
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| 195 |
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|
| 196 |
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|
| 197 |
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|
| 198 |
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|
| 199 |
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| 200 |
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| 201 |
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| 202 |
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| 203 |
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| 204 |
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| 205 |
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| 206 |
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|
| 207 |
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|
| 208 |
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| 209 |
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| 210 |
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| 211 |
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| 212 |
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| 213 |
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| 214 |
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| 215 |
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|
| 216 |
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| 217 |
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| 218 |
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| 219 |
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| 220 |
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| 221 |
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| 222 |
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| 223 |
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| 224 |
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| 225 |
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| 226 |
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| 227 |
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| 228 |
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| 229 |
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| 230 |
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| 231 |
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| 232 |
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| 233 |
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| 234 |
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| 235 |
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| 236 |
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| 237 |
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| 238 |
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| 239 |
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| 240 |
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| 241 |
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| 242 |
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| 243 |
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| 244 |
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| 245 |
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| 246 |
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| 247 |
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| 248 |
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| 249 |
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| 250 |
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| 251 |
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| 252 |
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| 253 |
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| 254 |
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| 255 |
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| 256 |
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| 257 |
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| 258 |
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| 259 |
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| 260 |
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| 261 |
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| 262 |
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| 263 |
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|
| 264 |
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| 265 |
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| 266 |
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| 267 |
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| 268 |
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| 269 |
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| 270 |
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|
| 271 |
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|
| 272 |
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|
| 273 |
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| 274 |
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| 275 |
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| 276 |
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| 277 |
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| 278 |
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|
| 279 |
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|
| 280 |
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|
| 281 |
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| 282 |
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| 283 |
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|
| 284 |
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| 285 |
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|
| 286 |
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|
| 287 |
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|
| 288 |
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|
| 289 |
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|
| 290 |
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|
| 291 |
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|
| 292 |
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|
| 293 |
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|
| 294 |
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|
| 295 |
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|
| 296 |
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|
| 297 |
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| 298 |
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|
| 299 |
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|
| 300 |
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| 301 |
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| 302 |
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|
| 303 |
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|
| 304 |
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|
| 305 |
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| 306 |
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| 307 |
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|
| 308 |
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| 309 |
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|
| 310 |
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|
| 311 |
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|
| 312 |
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|
| 313 |
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|
| 314 |
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|
| 315 |
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|
| 316 |
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|
| 317 |
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|
| 318 |
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|
| 319 |
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|
| 320 |
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|
| 321 |
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|
| 322 |
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|
| 323 |
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|
| 324 |
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| 325 |
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|
| 326 |
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|
| 327 |
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|
| 328 |
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| 329 |
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| 330 |
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| 331 |
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|
| 332 |
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| 333 |
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| 334 |
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|
| 335 |
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|
| 336 |
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| 337 |
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| 338 |
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|
| 339 |
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|
| 340 |
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| 341 |
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| 342 |
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|
| 343 |
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|
| 344 |
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| 345 |
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| 346 |
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| 347 |
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| 348 |
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| 349 |
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|
| 350 |
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| 351 |
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| 352 |
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| 353 |
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| 354 |
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| 355 |
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| 356 |
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| 357 |
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| 358 |
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| 359 |
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| 360 |
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| 361 |
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| 362 |
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| 363 |
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| 364 |
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| 365 |
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| 366 |
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| 367 |
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|
| 368 |
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| 369 |
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| 370 |
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| 371 |
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| 372 |
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| 373 |
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| 374 |
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| 375 |
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|
| 376 |
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| 377 |
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| 378 |
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| 379 |
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|
| 380 |
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| 381 |
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|
| 382 |
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|
| 383 |
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| 384 |
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| 385 |
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| 386 |
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| 387 |
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| 388 |
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| 389 |
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| 390 |
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|
| 391 |
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| 392 |
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| 393 |
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| 394 |
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| 395 |
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| 396 |
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| 397 |
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|
| 398 |
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|
| 399 |
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| 400 |
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| 401 |
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| 402 |
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| 403 |
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| 404 |
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| 405 |
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| 406 |
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|
| 407 |
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| 408 |
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| 409 |
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| 410 |
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| 411 |
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| 412 |
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| 413 |
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| 414 |
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"unk_token": "<unk>",
|
| 2106 |
+
"use_default_system_prompt": false,
|
| 2107 |
+
"use_fast": true
|
| 2108 |
+
}
|
vocabulary.json
ADDED
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|
|