初始化项目，由ModelHub XC社区提供模型
Model: pai/Qwen2-7B-Instruct-Exp Source: Original Platform
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README.md
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## 📖 Introduction
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**Qwen2-7B-Instruct-Exp** and **Qwen2-1.5B-Instruct-Exp** are powerful large language models that can expand instructions with same task type but of different content.
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We fine-tuned **Qwen2-7B-Instruct** and **Qwen2-1.5B-Instruct-Exp** to obtain **Qwen2-7B-Instruct-Exp** and **Qwen2-1.5B-Instruct-Exp**.
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We sampled the dataset from OpenHermes and the LCCD dataset, ensuring a balanced task distribution. For training set annotations, we used Qwen-max with incorporated our handwritten examples as in-context prompts.
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#### Example Input
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> Plan an in depth tour itinerary of France that includes Paris, Lyon, and Provence.
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#### Example Output 1
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> Describe a classic road trip itinerary along the California coastline in the United States.
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#### Example Output 2
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> Create a holiday plan that combines cultural experiences in Bangkok, Thailand, with beach relaxation in Phuket.
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## 🚀 Quick Start
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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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"alibaba-pai/Qwen2-7B-Instruct-Exp",
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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("alibaba-pai/Qwen2-7B-Instruct-Exp")
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prompt = "Give me a short introduction to large language model."
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messages = [
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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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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=2048，
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eos_token_id=151645，
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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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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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## 🔍 Evaluation
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We evaluated the data augmentation effect of our model on the Elementary Math and Implicature datasets.
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| Model | Math | Impl. |
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|--------------------------------|--------|--------|
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| Qwen2-1.5B-Instruct | 57.90% | 28.96% |
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| + Qwen2-1.5B-Instruct-Exp | 59.15% | 31.22% |
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| + Qwen2-7B-Instruct-Exp | 58.32% | 39.37% |
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| Qwen2-7B-Instruct | 71.40% | 28.85% |
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| + Qwen2-1.5B-Instruct-Exp | 73.90% | 35.41% |
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| + Qwen2-7B-Instruct-Exp | 72.53% | 32.92% |
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## 📜 Citation
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If you find our work helpful, please cite it!
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```
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@misc{data-augmentation-family,
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title={Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud},
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author={Yuanhao Yue and Chengyu Wang and Jun Huang and Peng Wang},
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year={2024},
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eprint={2412.04871},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2412.04871},
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}
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```
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config.json
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{
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"_name_or_path": "/mnt/workspace/yueyuanhao/202405/models/Qwen2-7B",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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|
||||
}
|
||||
}
|
||||
20
special_tokens_map.json
Normal file
20
special_tokens_map.json
Normal file
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a12e3ba5d5e0ad173cf7b408ab8534c6be8cbc6a146714e9c7dc8cf2346603b1
|
||||
size 7028043
|
||||
44
tokenizer_config.json
Normal file
44
tokenizer_config.json
Normal file
@@ -0,0 +1,44 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{% set system_message = 'You are a helpful assistant.' %}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user