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)
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