README.md

metadata

language:
  - ko
license: apache-2.0
library_name: adapter-transformers
base_model:
  - meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
  - MarkrAI/KOpen-HQ-Hermes-2.5-60K
pipeline_tag: text-generation
model-index:
  - name: naps-llama-3_1-8b-instruct-v0.4
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 73.44
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NAPS-ai/naps-llama-3_1-8b-instruct-v0.4
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 27.83
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NAPS-ai/naps-llama-3_1-8b-instruct-v0.4
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 17.22
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NAPS-ai/naps-llama-3_1-8b-instruct-v0.4
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 3.91
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NAPS-ai/naps-llama-3_1-8b-instruct-v0.4
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 13.96
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NAPS-ai/naps-llama-3_1-8b-instruct-v0.4
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 27.5
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=NAPS-ai/naps-llama-3_1-8b-instruct-v0.4
          name: Open LLM Leaderboard

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

unsloth를 사용하여 meta-llama/Meta-Llama-3.1-8B-Instruct 모델에 LORA 파인튜닝을 완료했습니다.

MarkrAI/KOpen-HQ-Hermes-2.5-60k 데이터를 학습시켰습니다.

How to use

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NAPS-ai/naps-llama-3.1-8b-instruct-v0.4")
model = AutoModelForCausalLM.from_pretrained("NAPS-ai/naps-llama-3.1-8b-instruct-v0.4")

Chatbot

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

tokenizer = AutoTokenizer.from_pretrained("NAPS-ai/naps-llama-3.1-8b-instruct-v0.4")
model = AutoModelForCausalLM.from_pretrained("NAPS-ai/naps-llama-3.1-8b-instruct-v0.4")




pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device=0,
)

def answering(question):
    messages = [
    {"role": "system", "content": "당신은 항상 친절하게 대답하는 안내원입니다."},
    {"role": "user", "content": question},
    ]
    outputs = pipeline(
    messages,
    max_new_tokens=1024,
    pad_token_id = pipeline.tokenizer.eos_token_id
    )
    return outputs[0]["generated_text"][2]['content']




while True:
    question = input("질문을 입력하세요 : ")
    if question == "종료":
        print("프로그램 종료")
        break
    answer = answering(question)
    print(f"AI의 답변: {answer}")

Contact : [email protected]

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	27.31
IFEval (0-Shot)	73.44
BBH (3-Shot)	27.83
MATH Lvl 5 (4-Shot)	17.22
GPQA (0-shot)	3.91
MuSR (0-shot)	13.96
MMLU-PRO (5-shot)	27.50