kyujinpy/CoT-llama-2k-7b

Model

stars

commits

linked in READMEs

Dec 2, 2024

updated

endpoints_compatible

llama

pytorch

safetensors

text-generation

text-generation-inference

transformers

README

(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다
The license is cc-by-nc-sa-4.0.

CoT-llama2-7B

More detail repo(Github): CoT-llama2

Model Details

Model Developers Kyujin Han (kyujinpy)

Input Models input text only.

Output Models generate text only.

Model Architecture

CoT-llama2 is an auto-regressive language model based on the LLaMA2 transformer architecture.

Base Model Llama-2-ko-7b

Training Dataset

I use KoCoT_2000.
Using DeepL, translate about kaist-CoT.

I use A100 GPU 40GB and COLAB, when trianing.

Training Hyperparameters

Hyperparameters	Value
batch_size	`64`
micro_batch_size	`1`
Epochs	`15`
learning_rate	`1e-5`
cutoff_len	`2048`
lr_scheduler	`linear`
base_model	`beomi/llama-2-ko-7b`

Model Benchmark

LM Eval Harness - Korean (polyglot branch)

Used EleutherAI's lm-evaluation-harness

Question Answering (QA)

COPA (F1)

Model	0-shot	5-shot	10-shot	50-shot
Polyglot-ko-1.3b	0.7196	0.7193	0.7204	0.7206
Polyglot-ko-3.8b	0.7595	0.7608	0.7638	0.7788
Polyglot-ko-5.8b	0.7745	0.7676	0.7775	0.7887
Polyglot-ko-12.8b	0.7937	0.8108	0.8037	0.8369
Llama-2-Ko-7b 20B	0.7388	0.7626	0.7808	0.7979
Llama-2-Ko-7b 40B	0.7436	0.7927	0.8037	0.8259
KO-platypus2-7B-EX	0.7509	0.7899	0.8029	0.8290
CoT-llama2-7B(ours)	0.7528	0.7888	0.7998	0.8210

Natural Language Inference (NLI; 자연어 추론 평가)

HellaSwag (F1)

Model	0-shot	5-shot	10-shot	50-shot
Polyglot-ko-1.3b	0.5247	0.5260	0.5278	0.5427
Polyglot-ko-3.8b	0.5707	0.5830	0.5670	0.5787
Polyglot-ko-5.8b	0.5976	0.5998	0.5979	0.6208
Polyglot-ko-12.8b	0.5954	0.6306	0.6098	0.6118
Llama-2-Ko-7b 20B	0.4518	0.4668	0.4726	0.4828
Llama-2-Ko-7b 40B	0.4562	0.4657	0.4698	0.4774
KO-platypus2-7B-EX	0.4571	0.4461	0.4371	0.4525
CoT-llama2-7B(ours)	0.4543	0.4554	0.4606	0.4579

Question Answering (QA)

BoolQ (F1)

Model	0-shot	5-shot	10-shot	50-shot
Polyglot-ko-1.3b	0.3552	0.4751	0.4109	0.4038
Polyglot-ko-3.8b	0.4320	0.5263	0.4930	0.4038
Polyglot-ko-5.8b	0.4356	0.5698	0.5187	0.5236
Polyglot-ko-12.8b	0.4818	0.6041	0.6289	0.6448
Llama-2-Ko-7b 20B	0.3607	0.6797	0.6801	0.6622
Llama-2-Ko-7b 40B	0.5786	0.6977	0.7084	0.7144
KO-platypus2-7B-EX	0.6028	0.6979	0.7016	0.6988
CoT-llama2-7B(ours)	0.5852	0.6947	0.7059	0.7213

Classification

SentiNeg (F1)

Model	0-shot	5-shot	10-shot	50-shot
Polyglot-ko-1.3b	0.6790	0.6257	0.5514	0.7851
Polyglot-ko-3.8b	0.4858	0.7950	0.7320	0.7851
Polyglot-ko-5.8b	0.3394	0.8841	0.8808	0.9521
Polyglot-ko-12.8b	0.9117	0.9015	0.9345	0.9723
Llama-2-Ko-7b 20B	0.4855	0.8295	0.8711	0.8513
Llama-2-Ko-7b 40B	0.4594	0.7611	0.7276	0.9370
KO-platypus2-7B-EX	0.5821	0.7653	0.7991	0.8643
CoT-llama2-7B(ours)	0.5045	0.8054	0.7942	0.9446

Implementation Code

### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "kyujinpy/CoT-llama-2k-7b"
CoT-llama = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
CoT-llama_tokenizer = AutoTokenizer.from_pretrained(repo)

Readme format: beomi/llama-2-ko-7b

Contributors

kyujinpy

16 commits

SFconvertbot

1 commits

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kyujinpy/CoT-llama-2k-7b

Model

stars

commits

linked in READMEs

Dec 2, 2024

updated

endpoints_compatible

llama

pytorch

safetensors

text-generation

text-generation-inference

transformers

README

(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다
The license is cc-by-nc-sa-4.0.

CoT-llama2-7B

More detail repo(Github): CoT-llama2

Model Details

Model Developers Kyujin Han (kyujinpy)

Input Models input text only.

Output Models generate text only.

Model Architecture

CoT-llama2 is an auto-regressive language model based on the LLaMA2 transformer architecture.

Base Model Llama-2-ko-7b

Training Dataset

I use KoCoT_2000.
Using DeepL, translate about kaist-CoT.

I use A100 GPU 40GB and COLAB, when trianing.

Training Hyperparameters

Hyperparameters	Value
batch_size	`64`
micro_batch_size	`1`
Epochs	`15`
learning_rate	`1e-5`
cutoff_len	`2048`
lr_scheduler	`linear`
base_model	`beomi/llama-2-ko-7b`

Model Benchmark

LM Eval Harness - Korean (polyglot branch)

Used EleutherAI's lm-evaluation-harness

Question Answering (QA)

COPA (F1)

Model	0-shot	5-shot	10-shot	50-shot
Polyglot-ko-1.3b	0.7196	0.7193	0.7204	0.7206
Polyglot-ko-3.8b	0.7595	0.7608	0.7638	0.7788
Polyglot-ko-5.8b	0.7745	0.7676	0.7775	0.7887
Polyglot-ko-12.8b	0.7937	0.8108	0.8037	0.8369
Llama-2-Ko-7b 20B	0.7388	0.7626	0.7808	0.7979
Llama-2-Ko-7b 40B	0.7436	0.7927	0.8037	0.8259
KO-platypus2-7B-EX	0.7509	0.7899	0.8029	0.8290
CoT-llama2-7B(ours)	0.7528	0.7888	0.7998	0.8210

Natural Language Inference (NLI; 자연어 추론 평가)

HellaSwag (F1)

Model	0-shot	5-shot	10-shot	50-shot
Polyglot-ko-1.3b	0.5247	0.5260	0.5278	0.5427
Polyglot-ko-3.8b	0.5707	0.5830	0.5670	0.5787
Polyglot-ko-5.8b	0.5976	0.5998	0.5979	0.6208
Polyglot-ko-12.8b	0.5954	0.6306	0.6098	0.6118
Llama-2-Ko-7b 20B	0.4518	0.4668	0.4726	0.4828
Llama-2-Ko-7b 40B	0.4562	0.4657	0.4698	0.4774
KO-platypus2-7B-EX	0.4571	0.4461	0.4371	0.4525
CoT-llama2-7B(ours)	0.4543	0.4554	0.4606	0.4579

Question Answering (QA)

BoolQ (F1)

Model	0-shot	5-shot	10-shot	50-shot
Polyglot-ko-1.3b	0.3552	0.4751	0.4109	0.4038
Polyglot-ko-3.8b	0.4320	0.5263	0.4930	0.4038
Polyglot-ko-5.8b	0.4356	0.5698	0.5187	0.5236
Polyglot-ko-12.8b	0.4818	0.6041	0.6289	0.6448
Llama-2-Ko-7b 20B	0.3607	0.6797	0.6801	0.6622
Llama-2-Ko-7b 40B	0.5786	0.6977	0.7084	0.7144
KO-platypus2-7B-EX	0.6028	0.6979	0.7016	0.6988
CoT-llama2-7B(ours)	0.5852	0.6947	0.7059	0.7213

Classification

SentiNeg (F1)

Model	0-shot	5-shot	10-shot	50-shot
Polyglot-ko-1.3b	0.6790	0.6257	0.5514	0.7851
Polyglot-ko-3.8b	0.4858	0.7950	0.7320	0.7851
Polyglot-ko-5.8b	0.3394	0.8841	0.8808	0.9521
Polyglot-ko-12.8b	0.9117	0.9015	0.9345	0.9723
Llama-2-Ko-7b 20B	0.4855	0.8295	0.8711	0.8513
Llama-2-Ko-7b 40B	0.4594	0.7611	0.7276	0.9370
KO-platypus2-7B-EX	0.5821	0.7653	0.7991	0.8643
CoT-llama2-7B(ours)	0.5045	0.8054	0.7942	0.9446

Implementation Code

### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "kyujinpy/CoT-llama-2k-7b"
CoT-llama = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
CoT-llama_tokenizer = AutoTokenizer.from_pretrained(repo)