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---
language:
- zh
license: apache-2.0
metrics:
- accuracy
pipeline_tag: text-generation
---
>> It's not a chat model, just using Wizard-LM-Chinese-instruct-evol datesets training with several steps for test the model typical Chinese skill，
>> this is version1, will release version2 for more long context windows and Chat model
>>____________________________
>>Train scenario:
>>2k context
>>datasets:Wizard-LM-Chinese-instruct-evol
>>batchsize:8
>>steps:500
>>epchos:2
>>____________________________________________________
>>How to use?
>>Follow common huggingface-api is enough or using other framework like VLLM, support continue training.
____________________________________________________
>>import transformers
>>import torch
>>model_id = "BoyangZ/llama3-chinese"
>>pipeline = transformers.pipeline(
>> "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
>> )
>>pipeline("川普和拜登谁能赢得大选??")
>> [{'generated_text': '川普和拜登谁能赢得大选?](https://www.voachinese.com'}]
>>
>> import torch
>> from transformers import AutoModelForCausalLM, AutoTokenizer
>> torch.set_default_device("cuda")
>> model = AutoModelForCausalLM.from_pretrained("BoyangZ/llama3-chinese", torch_dtype="auto", trust_remote_code=True)
>> tokenizer = AutoTokenizer.from_pretrained("BoyangZ/llama3-chinese", trust_remote_code=True)
>> inputs = tokenizer(
>> "川普和拜登一起竞选，美国总统,谁获胜的几率大,分析一下?",
>> return_tensors="pt",
>> return_attention_mask=False
>> )
>> outputs = model.generate(**inputs, max_length=200)
>> text = tokenizer.batch_decode(outputs)[0]
>> print(text)
>>Wechat:18618377979, Gmail:zhouboyang1983@gmail.com |