Model Card for Model ID
Model finetuned from llm-jp-3-13b
Model Details
- Base model: llm-jp/llm-jp-3-13b
- Dataset used for finetune: ichikara-instruction
- https://liat-aip.sakura.ne.jp/wp/llmのための日本語インストラクションデータ作成/llmのための日本語インストラクションデータ-公開/
- 関根聡, 安藤まや, 後藤美知子, 鈴木久美, 河原大輔, 井之上直也, 乾健太郎. ichikara-instruction: LLMのための日本語インストラクションデータの構築. 言語処理学会第30回年次大会(2024)
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Uses
Direct Use
1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
3
4model_id = "mithernet/llm-jp-3-13b-finetune"
5bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16)
6model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto")
7tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
8
9input = "りんご・トマト・ポストの共通点は？"
10
11prompt = f"""### 指示
12{input}
13### 回答
14"""
15tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
16with torch.no_grad():
17 outputs = model.generate(
18 tokenized_input,
19 attention_mask=torch.ones_like(tokenized_input),
20 max_new_tokens=100,
21 do_sample=False,
22 repetition_penalty=1.2,
23 pad_token_id=tokenizer.eos_token_id
24 )[0]
25output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
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