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metadata
tags:
  - text-generation
license: cc-by-nc-4.0
language:
  - ko
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
pipeline_tag: text-generation

DataVortexS-10.7B-dpo-v1.5

DataVortex

Model Details

Base Model

upstage/SOLAR-10.7B-Instruct-v1.0

Trained On

  • OS: Ubuntu 22.04
  • GPU: H100 80GB 4ea
  • transformers: v4.36.2

Instruction format

It follows ChatML format.

E.g.

text = """\
<|im_start|>system
당신은 μ‚¬λžŒλ“€μ΄ 정보λ₯Ό 찾을 수 μžˆλ„λ‘ λ„μ™€μ£ΌλŠ” 인곡지λŠ₯ λΉ„μ„œμž…λ‹ˆλ‹€.<|im_end|>
<|im_start|>user
λŒ€ν•œλ―Όκ΅­μ˜ μˆ˜λ„λŠ” μ–΄λ””μ•Ό?<|im_end|>
<|im_start|>assistant
λŒ€ν•œλ―Όκ΅­μ˜ μˆ˜λ„λŠ” μ„œμšΈμž…λ‹ˆλ‹€.<|im_end|>
<|im_start|>user
μ„œμšΈ μΈκ΅¬λŠ” 총 λͺ‡ λͺ…이야?<|im_end|>
<|im_start|>assistant
"""

Model Benchmark

Ko LM Eval Harness

Task 0-shot 5-shot 10-shot 50-shot
kobest_boolq 0.34687 0.930158 0.943013 0.938029
kobest_copa 0.693351 0.751805 0.75772 0.771704
kobest_hellaswag 0.480736 0.470852 0.474766 0.478576
kobest_sentineg 0.789423 0.962208 0.967241 0.964717
Average 0.577595 0.778756 0.785685 0.788257

Ko-LLM-Leaderboard

On Benchmarking ...

Average Ko-ARC Ko-HellaSwag Ko-MMLU Ko-TruthfulQA Ko-CommonGen V2
0 0 0 0 0 0

Implementation Code

This model contains the chat_template instruction format.
You can use the code below.

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.5")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.5")

messages = [
    {"role": "system", "content": "당신은 μ‚¬λžŒλ“€μ΄ 정보λ₯Ό 찾을 수 μžˆλ„λ‘ λ„μ™€μ£ΌλŠ” 인곡지λŠ₯ λΉ„μ„œμž…λ‹ˆλ‹€."},
    {"role": "user", "content": "λŒ€ν•œλ―Όκ΅­μ˜ μˆ˜λ„λŠ” μ–΄λ””μ•Ό?"},
    {"role": "assistant", "content": "λŒ€ν•œλ―Όκ΅­μ˜ μˆ˜λ„λŠ” μ„œμšΈμž…λ‹ˆλ‹€."},
    {"role": "user", "content": "μ„œμšΈ μΈκ΅¬λŠ” 총 λͺ‡ λͺ…이야?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

License

This model is licensed under the upstage/SOLAR-10.7B-Instruct-v1.0 license, with the cc-by-nc-4.0 license granted. Under this license, others are allowed to copy, modify, and share the work, as long as it is not used for commercial purposes. They must provide appropriate credit and distribute any derivative works under the same license. For more details, please refer to the cc-by-nc-4.0 license.