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--- |
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datasets: |
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- cahya/instructions-all |
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license: bigscience-bloom-rail-1.0 |
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language: |
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- de |
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- en |
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- es |
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- fr |
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- hi |
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- id |
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- ja |
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- ms |
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- pt |
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- ru |
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- th |
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- vi |
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- zh |
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pipeline_tag: text-generation |
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widget: |
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- text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous review as positive, neutral or negative?" |
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example_title: "zh-en sentiment" |
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- text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？" |
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example_title: "zh-zh sentiment" |
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- text: "Suggest at least five related search terms to \"Mạng neural nhân tạo\"." |
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example_title: "vi-en query" |
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- text: "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels»." |
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example_title: "fr-fr query" |
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- text: "Explain in a sentence in Telugu what is backpropagation in neural networks." |
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example_title: "te-en qa" |
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- text: "Why is the sky blue?" |
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example_title: "en-en qa" |
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- text: "Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is \"Heroes Come in All Shapes and Sizes\". Story (in Spanish):" |
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example_title: "es-en fable" |
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- text: "Write a fable about wood elves living in a forest that is suddenly invaded by ogres. The fable is a masterpiece that has achieved praise worldwide and its moral is \"Violence is the last refuge of the incompetent\". Fable (in Hindi):" |
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example_title: "hi-en fable" |
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--- |
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# Bloomz-7b1-instruct |
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This is Bloomz-7b1-mt model fine-tuned with multilingual instruction dataset and using Peft Lora fine-tuning. |
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Following languages are supported: English, German, French, Spanish, Hindi, Indonesian, Japanese, Malaysian, Portuguese, |
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Russian, Thai, Vietnamese and Chinese. |
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## Usage |
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Following is the code to do the inference using this model: |
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``` |
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import torch |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig |
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peft_model_id = "cahya/bloomz-7b1-instruct" |
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config = PeftConfig.from_pretrained(peft_model_id) |
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model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, |
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load_in_8bit=True, device_map='auto') |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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# Load the Lora model |
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model = PeftModel.from_pretrained(model, peft_model_id) |
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batch = tokenizer("User: How old is the universe?\nAssistant: ", return_tensors='pt').to(0) |
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with torch.cuda.amp.autocast(): |
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output_tokens = model.generate(**batch, max_new_tokens=200, |
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min_length=50, |
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do_sample=True, |
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top_k=40, |
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top_p=0.9, |
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temperature=0.2, |
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repetition_penalty=1.2, |
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num_return_sequences=1) |
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print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True)) |
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``` |
