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--- |
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license: apache-2.0 |
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language: |
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- id |
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tags: |
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- mistral |
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- text-generation-inference |
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--- |
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### mistral-indo-7b |
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[Mistral 7b v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) fine-tuned on [Indonesian's instructions dataset](https://huggingface.co/datasets/sarahlintang/Alpaca_indo_instruct). |
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### Prompt template: |
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``` |
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### Human: {Instruction}### Assistant: {response} |
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``` |
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### Example of Usage |
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``` |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer, GenerationConfig |
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model_id = "sarahlintang/mistral-indo-7b" |
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model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda") |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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def create_instruction(instruction): |
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prompt = f"### Human: {instruction} ### Assistant: " |
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return prompt |
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def generate( |
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instruction, |
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max_new_tokens=128, |
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temperature=0.1, |
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top_p=0.75, |
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top_k=40, |
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num_beams=4, |
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**kwargs |
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): |
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prompt = create_instruction(instruction) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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input_ids = inputs["input_ids"].to("cuda") |
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attention_mask = inputs["attention_mask"].to("cuda") |
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generation_config = GenerationConfig( |
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temperature=temperature, |
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top_p=top_p, |
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top_k=top_k, |
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num_beams=num_beams, |
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**kwargs, |
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) |
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with torch.no_grad(): |
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generation_output = model.generate( |
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input_ids=input_ids, |
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attention_mask=attention_mask, |
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generation_config=generation_config, |
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return_dict_in_generate=True, |
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output_scores=True, |
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max_new_tokens=max_new_tokens, |
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early_stopping=True |
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) |
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s = generation_output.sequences[0] |
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output = tokenizer.decode(s) |
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return output.split("### Assistant:")[1].strip() |
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instruction = "Sebutkan lima macam makanan khas Indonesia." |
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print(generate(instruction)) |
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``` |
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