Jolteon-Instruct-13B-alpha

The model was trained based on the EEVE-Korean-Instruct-10.8B-v1.0 model from yanolja, extended to 13.4b (12 layer pass-through) utilizing mergekit.

Methodology

TBD

Training Details

Training Data Parameters Content Length Samples Seen Learning Rate
Jolteon-Instruct-13B-alpha A curated mix of English + Korean Instruction set 13.4B 4k >850k 1e-5

Example

Inference Code

from vllm import LLM, SamplingParams
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

llm = LLM(model="maywell/Jolteon-Instruct-13B-alpha", tensor_parallel_size=1, max_model_len=4096, gpu_memory_utilization=0.95)
sampling_params = SamplingParams(temperature=0.6, top_p=0.3, top_k=40, max_tokens=4096)
template = """ Below is an instruction that describes a task. Write a response that appropriately completes the request.

 ### Instruction: {0}

 ### Response: """

outputs = llm.generate([template.format("Meta(๊ตฌ, ํŽ˜์ด์Šค๋ถ)์˜ ์˜คํ”ˆ์†Œ์Šค AI ๊ธฐ์—ฌ๋ฅผ ์ฐฌ์–‘ํ•˜๋Š” ๋งํˆฌ๋กœ, Meta ์ง€์ง€ํ•œ๋‹ค๋Š” ์ทจ์ง€์˜ ์„ฑ๋ช…๋ฌธ์„ ์ž‘์„ฑํ•ด๋ด.")], sampling_params)
print(outputs[0].outputs[0].text.strip())

Example Output

๐ŸŒŸ Meta์˜ OpenAI ๊ธฐ์—ฌ: ํ˜์‹ ๊ณผ ํฌ์šฉ์„ ์œ„ํ•œ ํ˜์‹ ์ ์ธ ์ ‘๊ทผ ๋ฐฉ์‹ ๐ŸŒŸ

์šฐ๋ฆฌ๋Š” Meta(๊ตฌ, ํŽ˜์ด์Šค๋ถ)์˜ ์˜คํ”ˆ์†Œ์Šค AI(์ธ๊ณต์ง€๋Šฅ) ๊ธฐ์—ฌ๋ฅผ ์—ด๋ ฌํžˆ ์ง€์ง€ํ•ฉ๋‹ˆ๋‹ค. ์ด ํšŒ์‚ฌ๋Š” ํ˜์‹ ๊ณผ ํฌ์šฉ์„ ์œ„ํ•œ ํ˜์‹ ์ ์ธ ์ ‘๊ทผ ๋ฐฉ์‹์„ ๋ณด์—ฌ์ฃผ๋ฉฐ, AI ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์„ ์œ„ํ•ด ์ง€์†์ ์œผ๋กœ ๋…ธ๋ ฅํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.

Meta๋Š” ์˜คํ”ˆ์†Œ์Šค AI ์ปค๋ฎค๋‹ˆํ‹ฐ์— ์ ๊ทน์ ์œผ๋กœ ์ฐธ์—ฌํ•˜๋ฉฐ, ๋‹ค์–‘ํ•œ ํ”„๋กœ์ ํŠธ์™€ ํ”Œ๋žซํผ์„ ํ†ตํ•ด ์ง€์‹๊ณผ ๊ธฐ์ˆ ์„ ๊ณต์œ ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ AI ๊ธฐ์ˆ ์˜ ๋ฐœ์ „๊ณผ ํ•จ๊ป˜ ์‚ฌํšŒ์ , ๊ฒฝ์ œ์  ๋ฐœ์ „์—๋„ ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นฉ๋‹ˆ๋‹ค.

Meta์˜ ์˜คํ”ˆ์†Œ์Šค AI ๊ธฐ์—ฌ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๋ฉด์—์„œ ๋งค์šฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค:

1. ํ˜์‹  ์ด‰์ง„: Meta๋Š” ์˜คํ”ˆ์†Œ์Šค AI ํ”Œ๋žซํผ์„ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ์ž์™€ ๊ฐœ๋ฐœ์ž๋“ค์ด ์ตœ์‹  AI ๊ธฐ์ˆ ์„ ํƒ๊ตฌํ•˜๊ณ  ์‹คํ—˜ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์ด๋Š” ์ƒˆ๋กœ์šด ์•„์ด๋””์–ด์™€ ํ˜์‹ ์ ์ธ ๊ธฐ์ˆ ์˜ ์ถœํ˜„์„ ์ด‰์ง„ํ•ฉ๋‹ˆ๋‹ค.

2. ํฌ์šฉ ์ฆ์ง„: ์˜คํ”ˆ์†Œ์Šค AI๋Š” ๋ชจ๋“  ์‚ฌ๋žŒ์ด AI ๊ธฐ์ˆ ์˜ ์ด์ ์„ ๋ˆ„๋ฆด ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋ฉฐ, ์ด๋Š” ์‚ฌํšŒ์  ํฌ์šฉ์„ ์ฆ์ง„์‹œํ‚ต๋‹ˆ๋‹ค. ๋‹ค์–‘ํ•œ ๋ฐฐ๊ฒฝ๊ณผ ๊ฒฝํ—˜์„ ๊ฐ€์ง„ ์‚ฌ๋žŒ๋“ค์ด AI ๊ธฐ์ˆ ์„ ๋ฐฐ์šฐ๊ณ  ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๋ฅผ ๊ฐ€์ง€๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.

3. ํˆฌ๋ช…์„ฑ๊ณผ ์‹ ๋ขฐ: ์˜คํ”ˆ์†Œ์Šค AI๋Š” ๊ธฐ์ˆ ์˜ ์ž‘๋™ ์›๋ฆฌ๋ฅผ ๊ณต๊ฐœํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์‚ฌ์šฉ์ž์™€ ์‚ฌํšŒ์— ๋Œ€ํ•œ ์‹ ๋ขฐ๋ฅผ ๊ตฌ์ถ•ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Š” AI ๊ธฐ์ˆ ์˜ ์‚ฌํšŒ์  ์ˆ˜์šฉ๋„๋ฅผ ๋†’์ด๋Š” ๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค.

Meta์˜ ์˜คํ”ˆ์†Œ์Šค AI ๊ธฐ์—ฌ๋Š” ํ˜์‹ ๊ณผ ํฌ์šฉ์„ ์œ„ํ•œ ํ˜์‹ ์ ์ธ ์ ‘๊ทผ ๋ฐฉ์‹์„ ๋ณด์—ฌ์ฃผ๋ฉฐ, ์ด๋Š” ์šฐ๋ฆฌ๊ฐ€ ๋” ๋‚˜์€ ๋ฏธ๋ž˜๋ฅผ ์œ„ํ•œ ์ค‘์š”ํ•œ ๋‹จ๊ณ„์ž…๋‹ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ด๋Ÿฌํ•œ ๋…ธ๋ ฅ์„ ์ง€์ง€ํ•˜๋ฉฐ, ๋” ๋งŽ์€ ๊ธฐ์—…๊ณผ ์กฐ์ง์ด ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ฑ„ํƒํ•˜๊ธธ ๋ฐ”๋ž๋‹ˆ๋‹ค. ํ•จ๊ป˜ ๋” ๋‚˜์€ ๋ฏธ๋ž˜๋ฅผ ๋งŒ๋“ค์–ด ๋‚˜๊ฐ€์š”!

License

๋ณธ ๋ชจ๋ธ์€ apache-2.0 ๋ผ์ด์„ผ์Šค๋ฅผ ๋”ฐ๋ฆ…๋‹ˆ๋‹ค. ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ƒ์„ฑ๋œ ๋ฐ์ดํ„ฐ์…‹์„ ๋ฐฐํฌํ•  ๊ฒฝ์šฐ ๋ชจ๋ธ ์‚ฌ์šฉ์„ ๋ช…์‹œํ•ด ์ฃผ์‹œ๊ธฐ๋ฅผ ๊ถŒ๊ณ ๋“œ๋ฆฝ๋‹ˆ๋‹ค.

Thanks to

  • A100 ํด๋Ÿฌ์Šคํ„ฐ๋ฅผ ์ œ๊ณตํ•ด์ฃผ์‹ , Sionic AI

Contact

Downloads last month
2,228
Safetensors
Model size
13.4B params
Tensor type
BF16
ยท
Inference Providers NEW
This model is not currently available via any of the supported third-party Inference Providers, and the model is not deployed on the HF Inference API.

Model tree for maywell/Jolteon-Instruct-13B-alpha

Finetuned
(20)
this model
Quantizations
3 models