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---
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license: apache-2.0
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datasets:
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- wangrui6/Zhihu-KOL
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language:
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- zh
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base_model:
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- lucky2me/Dorami
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---
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# Dorami-Instruct
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Dorami-Instruct is a Supervised Fine-tuning(SFT) model based on the pretrained model lucky2me/Dorami
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## Model description
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### Training data
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- [wangrui6/Zhihu-KOL](https://huggingface.co/datasets/wangrui6/Zhihu-KOL)
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### Training code
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- [dorami](https://github.com/6zeus/dorami.git)
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## How to use
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### 1. Download model from Hugging Face Hub to local
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```
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git lfs install
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git clone https://huggingface.co/lucky2me/Dorami-Instruct
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```
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### 2. Use the model downloaded above
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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model_path = "The path of the model downloaded above"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(model_path)
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prompt="fill in any prompt you like."
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inputs = tokenizer(prompt, return_tensors="pt")
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generation_config = GenerationConfig(max_new_tokens=64, do_sample=True, top_k=2, eos_token_id=model.config.eos_token_id)
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outputs = model.generate(**inputs, generation_config=generation_config)
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decoded_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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print(decoded_text)
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``` |
