base_model: llm-jp/llm-jp-3-13b
library_name: peft
license: apache-2.0
tags:
- unsloth
- Transformers
- trl
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How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForCausalLM,AutoTokenizer,BitsAndBytesConfig
from peft import PeftModel,PeftConfig
import torch
HF_TOKEN = "your token"
model_name = "llm-jp/llm-jp-3-13b"
adapter_name = "yossy0125/llm-jp-3-13b-it_lora/"
#QLoRaの量子化に合わせる
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type= "nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
#BaseModel
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config = bnb_config,
device_map="auto",
token=HF_TOKEN
)
tokenizer = AutoTokenizer.from_pretrained(model_name,trust_remote_code=True,token=HF_TOKEN)
#adapterをBaseModelに統合
model = PeftModel.from_pretrained(model,adapter_name,token=HF_TOKEN)
input = "カレーの具材は何ですか？"
prompt = f"""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出力しなさい。
### 指示:{input}
### 応答:
"""
tokenized_input = tokenizer.encode(prompt,add_special_tokens=False,return_tensors="pt").to(model.device)
attention_mask = torch.ones_like(tokenized_input)
outputs = None
with torch.no_grad():
outputs = model.generate(
tokenized_input,
attention_mask=attention_mask,
max_new_tokens=2048, #生成するトークン数
do_sample=False,
repetition_penalty=1.2,
pad_token_id=tokenizer.eos_token_id
)[0]
output = tokenizer.decode(outputs[tokenized_input.size(1):],skip_special_tokens=True)
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Training Details
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Framework versions
- PEFT 0.13.2