73 lines
1.9 KiB
Markdown
73 lines
1.9 KiB
Markdown
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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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