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README.md
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@@ -56,30 +56,34 @@ Checkpoints format: Hugging Face Transformers (Megatron-DeepSpeed format models
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## Required Libraries and Their Versions
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- torch>=2.
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- transformers>=4.
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- tokenizers>=0.
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- accelerate
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## Usage
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-13b-instruct-full-
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model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-13b-instruct-full-
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output = model.generate(
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tokenized_input,
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max_new_tokens=512,
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do_sample=True,
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top_p=0.95,
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temperature=0.7,
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repetition_penalty=1.
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)[0]
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print(tokenizer.decode(output))
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```
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## Required Libraries and Their Versions
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- torch>=2.3.0
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- transformers>=4.40.1
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- tokenizers>=0.19.1
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- accelerate>=0.29.3
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- flash-attn>=2.5.8
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## Usage
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0")
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model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0", device_map="auto", torch_dtype=torch.float16)
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chat = [
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{"role": "system", "content": "以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。"},
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{"role": "user", "content": "自然言語処理とは何か"},
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]
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tokenized_input = tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, return_tensors="pt").to(model.device)
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with torch.inference_mode():
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output = model.generate(
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tokenized_input,
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max_new_tokens=512,
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do_sample=True,
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top_p=0.95,
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temperature=0.7,
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repetition_penalty=1.05,
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)[0]
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print(tokenizer.decode(output, skip_special_tokens=True))
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```
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