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--- |
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license: apache-2.0 |
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datasets: |
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- heegyu/kowikitext |
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- heegyu/kowiki-sentences |
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language: |
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- ko |
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- en |
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library_name: transformers |
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tags: |
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- pytorch |
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--- |
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Experimental Repository :) |
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Here's some test: |
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```python |
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from transformers import pipeline |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model = AutoModelForCausalLM.from_pretrained( |
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'beomi/Mistral-Ko-Inst-dev', |
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torch_dtype='auto', |
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device_map='auto', |
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) |
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tokenizer = AutoTokenizer.from_pretrained('beomi/Mistral-Ko-Inst-dev') |
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pipe = pipeline( |
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'text-generation', |
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model=model, |
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tokenizer=tokenizer, |
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do_sample=True, |
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max_new_tokens=350, |
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return_full_text=False, |
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no_repeat_ngram_size=6, |
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eos_token_id=1, # not yet tuned to gen </s>, use <s> instead. |
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) |
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def gen(x): |
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chat = tokenizer.apply_chat_template([ |
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{"role": "user", "content": x}, |
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# {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, |
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# {"role": "user", "content": "Do you have mayonnaise recipes? please say in Korean."} |
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], tokenize=False) |
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print(pipe(chat)[0]['generated_text'].strip()) |
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gen("μ€νλ²
μ€μ μ€νλ²
μ€ μ½λ¦¬μμ μ°¨μ΄λ?") |
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# (μμ± μμ) |
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# μ€νλ²
μ€λ μ μΈκ³μ μΌλ‘ μ΄μνκ³ μλ μ»€νΌ μ λ¬Έμ¬μ΄λ€. νκ΅μλ μ€νλ²
μ€ μ½λ¦¬μλΌλ μ΄λ¦μΌλ‘ μ΄μλκ³ μλ€. |
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# μ€νλ²
μ€ μ½λ¦¬μλ λνλ―Όκ΅μ μ
μ ν μ΄ν 2009λ
κ³Ό 2010λ
μ λ μ°¨λ‘μ λΈλλκ³Όμ μ¬κ²ν λ° μλ‘μ΄ λμμΈμ ν΅ν΄ μλ‘μ΄ λΈλλλ€. μ»€νΌ μ λ¬Έμ ν리미μ μ΄λ―Έμ§λ₯Ό μ μ§νκ³ μκ³ , μ€νλ²
μ€ μ½λ¦¬μλ νκ΅μ λννλ ν리미μ μ»€νΌ μ λ¬Έ λΈλλμ λ§λ€κ³ μλ€. |
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``` |