Instructions to use Ash-Hun/WelSSiSKo_v3_llama-2-ko-base_text-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ash-Hun/WelSSiSKo_v3_llama-2-ko-base_text-generation with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("beomi/llama-2-ko-7b") model = PeftModel.from_pretrained(base_model, "Ash-Hun/WelSSiSKo_v3_llama-2-ko-base_text-generation") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Ash-Hun/WelSSiSKo_v3_llama-2-ko-base_text-generation: direct link, hf CLI and curl.
- Browser
- Download file 1.4 kB
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https://huggingface.co/Ash-Hun/WelSSiSKo_v3_llama-2-ko-base_text-generation/resolve/main/README.md
- Command line
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hf download hf://Ash-Hun/WelSSiSKo_v3_llama-2-ko-base_text-generation/README.md
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curl -L -o README.md https://huggingface.co/Ash-Hun/WelSSiSKo_v3_llama-2-ko-base_text-generation/resolve/main/README.md
1.4 kB
metadata
license: llama2
base_model: beomi/llama-2-ko-7b
inference: false
datasets:
- Ash-Hun/Welfare-QA
library_name: peft
pipeline_tag: text-generation
tags:
- torch
- llama2
- domain-specific-lm
"WelSSiSKo : Welfare Domain Specific Model"
Github ▼
If you want to get how to use this model, please check my github repository :)
👉 Github Repo
What is BaseModel ▼
Training procedure ▼
The following bitsandbytes quantization config was used during training:
- load_in_4bit: True
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
Framework versions ▼
- PEFT 0.8.2.
Evaluate Score
- 적절한 Domain Benchmark Set이 없기때문에 정성평가를 진행하였고 그에 따른 AVG Score는 0.74 입니다.
