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Duplicate from Qwen/Qwen2-72B-Instruct
Browse filesCo-authored-by: Yang Fan <clonefy@users.noreply.huggingface.co>
- .gitattributes +35 -0
- LICENSE +53 -0
- README.md +169 -0
- config.json +27 -0
- generation_config.json +14 -0
- merges.txt +0 -0
- model-00001-of-00037.safetensors +3 -0
- model-00002-of-00037.safetensors +3 -0
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- model-00037-of-00037.safetensors +3 -0
- model.safetensors.index.json +970 -0
- tokenizer.json +0 -0
- tokenizer_config.json +40 -0
- vocab.json +0 -0
.gitattributes
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LICENSE
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| 1 |
+
Tongyi Qianwen LICENSE AGREEMENT
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Tongyi Qianwen Release Date: August 3, 2023
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By clicking to agree or by using or distributing any portion or element of the Tongyi Qianwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
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d. "Third Parties" shall mean individuals or legal entities that are not under common control with Us or You.
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e. "Tongyi Qianwen" shall mean the large language models (including Qwen model and Qwen-Chat model), and software and algorithms, consisting of trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Us.
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b. You can not use the Materials or any output therefrom to improve any other large language model (excluding Tongyi Qianwen or derivative works thereof).
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a. The term of this Agreement shall commence upon your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
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b. We may terminate this Agreement if you breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, you must delete and cease use of the Materials. Sections 7 and 9 shall survive the termination of this Agreement.
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a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
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README.md
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---
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license: other
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license_name: tongyi-qianwen
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license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE
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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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- chat
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---
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# Qwen2-72B-Instruct
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## Introduction
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Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 72B Qwen2 model.
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Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
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Qwen2-72B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts.
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For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/).
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<br>
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## Model Details
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Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.
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## Training details
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We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.
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## Requirements
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The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
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```
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KeyError: 'qwen2'
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```
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## Quickstart
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Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained(
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"Qwen/Qwen2-72B-Instruct",
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-72B-Instruct")
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prompt = "Give me a short introduction to large language model."
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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| 64 |
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| 65 |
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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| 73 |
+
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
| 74 |
+
```
|
| 75 |
+
|
| 76 |
+
### Processing Long Texts
|
| 77 |
+
|
| 78 |
+
To handle extensive inputs exceeding 32,768 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
|
| 79 |
+
|
| 80 |
+
For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps:
|
| 81 |
+
|
| 82 |
+
1. **Install vLLM**: You can install vLLM by running the following command.
|
| 83 |
+
|
| 84 |
+
```bash
|
| 85 |
+
pip install "vllm>=0.4.3"
|
| 86 |
+
```
|
| 87 |
+
|
| 88 |
+
Or you can install vLLM from [source](https://github.com/vllm-project/vllm/).
|
| 89 |
+
|
| 90 |
+
2. **Configure Model Settings**: After downloading the model weights, modify the `config.json` file by including the below snippet:
|
| 91 |
+
```json
|
| 92 |
+
{
|
| 93 |
+
"architectures": [
|
| 94 |
+
"Qwen2ForCausalLM"
|
| 95 |
+
],
|
| 96 |
+
// ...
|
| 97 |
+
"vocab_size": 152064,
|
| 98 |
+
|
| 99 |
+
// adding the following snippets
|
| 100 |
+
"rope_scaling": {
|
| 101 |
+
"factor": 4.0,
|
| 102 |
+
"original_max_position_embeddings": 32768,
|
| 103 |
+
"type": "yarn"
|
| 104 |
+
}
|
| 105 |
+
}
|
| 106 |
+
```
|
| 107 |
+
This snippet enable YARN to support longer contexts.
|
| 108 |
+
|
| 109 |
+
3. **Model Deployment**: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command:
|
| 110 |
+
|
| 111 |
+
```bash
|
| 112 |
+
python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-72B-Instruct --model path/to/weights
|
| 113 |
+
```
|
| 114 |
+
|
| 115 |
+
Then you can access the Chat API by:
|
| 116 |
+
|
| 117 |
+
```bash
|
| 118 |
+
curl http://localhost:8000/v1/chat/completions \
|
| 119 |
+
-H "Content-Type: application/json" \
|
| 120 |
+
-d '{
|
| 121 |
+
"model": "Qwen2-72B-Instruct",
|
| 122 |
+
"messages": [
|
| 123 |
+
{"role": "system", "content": "You are a helpful assistant."},
|
| 124 |
+
{"role": "user", "content": "Your Long Input Here."}
|
| 125 |
+
]
|
| 126 |
+
}'
|
| 127 |
+
```
|
| 128 |
+
|
| 129 |
+
For further usage instructions of vLLM, please refer to our [Github](https://github.com/QwenLM/Qwen2).
|
| 130 |
+
|
| 131 |
+
**Note**: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required.
|
| 132 |
+
|
| 133 |
+
## Evaluation
|
| 134 |
+
|
| 135 |
+
We briefly compare Qwen2-72B-Instruct with similar-sized instruction-tuned LLMs, including our previous Qwen1.5-72B-Chat. The results are shown as follows:
|
| 136 |
+
|
| 137 |
+
| Datasets | Llama-3-70B-Instruct | Qwen1.5-72B-Chat | **Qwen2-72B-Instruct** |
|
| 138 |
+
| :--- | :---: | :---: | :---: |
|
| 139 |
+
| _**English**_ | | | |
|
| 140 |
+
| MMLU | 82.0 | 75.6 | **82.3** |
|
| 141 |
+
| MMLU-Pro | 56.2 | 51.7 | **64.4** |
|
| 142 |
+
| GPQA | 41.9 | 39.4 | **42.4** |
|
| 143 |
+
| TheroemQA | 42.5 | 28.8 | **44.4** |
|
| 144 |
+
| MT-Bench | 8.95 | 8.61 | **9.12** |
|
| 145 |
+
| Arena-Hard | 41.1 | 36.1 | **48.1** |
|
| 146 |
+
| IFEval (Prompt Strict-Acc.) | 77.3 | 55.8 | **77.6** |
|
| 147 |
+
| _**Coding**_ | | | |
|
| 148 |
+
| HumanEval | 81.7 | 71.3 | **86.0** |
|
| 149 |
+
| MBPP | **82.3** | 71.9 | 80.2 |
|
| 150 |
+
| MultiPL-E | 63.4 | 48.1 | **69.2** |
|
| 151 |
+
| EvalPlus | 75.2 | 66.9 | **79.0** |
|
| 152 |
+
| LiveCodeBench | 29.3 | 17.9 | **35.7** |
|
| 153 |
+
| _**Mathematics**_ | | | |
|
| 154 |
+
| GSM8K | **93.0** | 82.7 | 91.1 |
|
| 155 |
+
| MATH | 50.4 | 42.5 | **59.7** |
|
| 156 |
+
| _**Chinese**_ | | | |
|
| 157 |
+
| C-Eval | 61.6 | 76.1 | **83.8** |
|
| 158 |
+
| AlignBench | 7.42 | 7.28 | **8.27** |
|
| 159 |
+
|
| 160 |
+
## Citation
|
| 161 |
+
|
| 162 |
+
If you find our work helpful, feel free to give us a cite.
|
| 163 |
+
|
| 164 |
+
```
|
| 165 |
+
@article{qwen2,
|
| 166 |
+
title={Qwen2 Technical Report},
|
| 167 |
+
year={2024}
|
| 168 |
+
}
|
| 169 |
+
```
|
config.json
ADDED
|
@@ -0,0 +1,27 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Qwen2ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_dropout": 0.0,
|
| 6 |
+
"bos_token_id": 151643,
|
| 7 |
+
"eos_token_id": 151645,
|
| 8 |
+
"hidden_act": "silu",
|
| 9 |
+
"hidden_size": 8192,
|
| 10 |
+
"initializer_range": 0.02,
|
| 11 |
+
"intermediate_size": 29568,
|
| 12 |
+
"max_position_embeddings": 32768,
|
| 13 |
+
"max_window_layers": 80,
|
| 14 |
+
"model_type": "qwen2",
|
| 15 |
+
"num_attention_heads": 64,
|
| 16 |
+
"num_hidden_layers": 80,
|
| 17 |
+
"num_key_value_heads": 8,
|
| 18 |
+
"rms_norm_eps": 1e-06,
|
| 19 |
+
"rope_theta": 1000000.0,
|
| 20 |
+
"sliding_window": 131072,
|
| 21 |
+
"tie_word_embeddings": false,
|
| 22 |
+
"torch_dtype": "bfloat16",
|
| 23 |
+
"transformers_version": "4.40.1",
|
| 24 |
+
"use_cache": true,
|
| 25 |
+
"use_sliding_window": false,
|
| 26 |
+
"vocab_size": 152064
|
| 27 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 151643,
|
| 3 |
+
"pad_token_id": 151643,
|
| 4 |
+
"do_sample": true,
|
| 5 |
+
"eos_token_id": [
|
| 6 |
+
151645,
|
| 7 |
+
151643
|
| 8 |
+
],
|
| 9 |
+
"repetition_penalty": 1.05,
|
| 10 |
+
"temperature": 0.7,
|
| 11 |
+
"top_p": 0.8,
|
| 12 |
+
"top_k": 20,
|
| 13 |
+
"transformers_version": "4.37.0"
|
| 14 |
+
}
|
merges.txt
ADDED
|
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|
|
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| 970 |
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{
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"add_prefix_space": false,
|
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"added_tokens_decoder": {
|
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"151643": {
|
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|
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|
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|
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|
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"special": true
|
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},
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"151644": {
|
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"content": "<|im_start|>",
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"lstrip": false,
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"rstrip": false,
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"single_word": false,
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"special": true
|
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},
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"151645": {
|
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"content": "<|im_end|>",
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"lstrip": false,
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"rstrip": false,
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"single_word": false,
|
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"special": true
|
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}
|
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},
|
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+
"additional_special_tokens": ["<|im_start|>", "<|im_end|>"],
|
| 30 |
+
"bos_token": null,
|
| 31 |
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"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
| 32 |
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"clean_up_tokenization_spaces": false,
|
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"eos_token": "<|im_end|>",
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"errors": "replace",
|
| 35 |
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"model_max_length": 131072,
|
| 36 |
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"pad_token": "<|endoftext|>",
|
| 37 |
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|
| 38 |
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"tokenizer_class": "Qwen2Tokenizer",
|
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|
| 40 |
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}
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