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library_name: transformers
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tags: []
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---
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##
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<!-- Provide a longer summary of what this model is. -->
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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## 模型描述
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使用 [hfl/chinese-llama-2-7b · Hugging Face](https://huggingface.co/hfl/chinese-llama-2-7b) 作为中文分词器,训练的 Mixtral-4x7B-MoE 模型。
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可以在单卡 A100 上推理,在 8xA100 上全量微调。
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## 部分评测指标
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| MMLU | CMMLU | C-Eval | GSM8K | MBPP |
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| ----- | ----- | ------ | ----- | ---- |
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| 55.13 | 51.10 | 52.0 | 67.17 | 40.2 |
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## 使用方式
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```python
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import torch
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import transformers
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def apply_multi_turn_template(history, input):
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multi_turn_template = "[ROUND {} USER]{}[ROUND {} ASSISTANT]{}"
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prefix = ""
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for i in range(len(history)):
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prefix = prefix + multi_turn_template.format(i, history[i][0], i, history[i][1])
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prefix = prefix + "<|end_of_turn|>"
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curr_turn_id = len(history)
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curr_turn = "[ROUND {} USER]{}[ROUND {} ASSISTANT]".format(curr_turn_id, input, curr_turn_id)
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return prefix + curr_turn
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model = transformers.AutoModelForCausalLM.from_pretrained("XuYipei/Mixtral-4x7B-Chat-Chinese")
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tokenizer = transformers.AutoTokenizer.from_pretrained("XuYipei/Mixtral-4x7B-Chat-Chinese")
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model = model.to(torch.bfloat16).to("cuda")
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input_text = apply_multi_turn_template([], "列举出10首杜甫的古诗,同时默写该诗篇的完整内容。")
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output = md.generate(input_ids = torch.LongTensor([tk.encode(input_text)]).to("cuda:5"), eos_token_id=55296, max_length=4096)
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output_text = tokenizer.decode(output[0])
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'''
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<s> [ROUND 0 USER]列举出10首杜甫的古诗,同时默写该诗篇的完整内容。[ROUND 0 ASSISTANT] 1. 《登高》
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唐·杜甫
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风急天高猿啸哀,渚清沙白鸟飞回。
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无边落木萧萧下,不尽长江滚滚来。
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万里悲秋常作客,百年多病独登台。
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艰难苦恨繁霜鬓,潦倒新停浊酒杯。
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2. 《春夜喜雨》
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唐·杜甫
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好雨知时节,当春乃发生。
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随风潜入夜,润物细无声。
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野径云俱黑,江船火独明。
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晓看红湿处,花重锦官城。
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3. 《绝句》
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唐·杜甫
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迟日江山丽,春风花草香。
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泥融飞燕子,沙暖睡鸳鸯。
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4. 《登楼》
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唐·杜甫
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花近高楼伤客心,万方多难此登临。
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锦江春色来天地,玉垒浮云变古今。
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北极朝廷终不改,西山寇盗莫相侵。
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可怜后主还祠庙,日暮聊为梁甫吟。
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5. 《茅屋为秋风所破歌》
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唐·杜甫
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八月秋高风怒号,卷我屋上三重茅。
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茅飞渡江洒江郊,高者挂罥长林梢,
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下者飘转沉塘坳。
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南村群童欺我老无力,忍能对面为盗贼。
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公然抱茅入竹去,唇焦口燥呼不得,
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归来倚杖自叹息。
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6. 《江南逢李龟年》
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唐·杜甫
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岐王宅里寻常见,崔九堂前几度闻。
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正是江南好风景,落花时节又逢君。
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7. 《闻官军收河南河北》
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唐·杜甫
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剑外忽传收蓟北,初闻涕泪满衣裳。
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却看妻子愁何在,漫卷诗书喜欲��。
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白日放歌须纵酒,青春作伴好还乡。
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即从巴峡穿巫峡,便下襄阳向洛阳。
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8. 《旅夜书怀》
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唐·杜甫
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细草微风岸,危樯独夜舟。
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星垂平野阔,月涌大江流。
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名岂文章著,官应老病休。
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飘飘何所似,天地一沙鸥。
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9. 《江畔独步寻花》
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唐·杜甫
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黄四娘家花满蹊,千朵万朵压枝低。
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留恋戏蝶时时舞,自在娇莺恰恰啼。
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10. 《赠花卿》
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唐·杜甫
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锦城丝管日纷纷,半入江风半入云。
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此曲只应天上有,人间能得几回闻。<|end_of_turn|>
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'''
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```
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