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  • Developed by: taoki
  • License: apache-2.0
  • Finetuned from model : tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

model_name = "tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True, torch_dtype=torch.bfloat16)

model = PeftModel.from_pretrained(model, "taoki/Swallow-MX-8x7b-NVE-v0.1-qlora-amenokaku-code-adapter")

prompt="""### Instruction:
紫式部と清少納言の作風をjsonで出力してください。
### Response:
"""

input_ids = tokenizer.encode(
    prompt,
    add_special_tokens=False,
    return_tensors="pt"
)
tokens = model.generate(
    input_ids.to(device=model.device),
    max_new_tokens=1024,
    temperature=0.99,
    top_p=0.95,
    do_sample=True,
)

out = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(out)

Output

### Instruction:
紫式部と清少納言の作風をjsonで出力してください。
### Response:
```json
{
    "紫式部": "貴人に会って、その人が話していることを思い出しながら奏でると、これにまさる楽器はありません。」,
    "清少納言": "人によってあげくはなく、おのずからかなしくゆくほどに、かなしみは深くなりゆきなさるなり。」
}
```

Framework versions

  • PEFT 0.9.0
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