Synatra-7B-Instruct-v0.2-8bpw-h8-exl2

exl2 version of maywell/Synatra-7B-Instruct-v0.2
used dataset : beomi/KoAlpaca
quantized by IHaBiS

command : python convert.py -i models/maywell_Synatra-7B-Instruct-v0.2 -o Synatra-7B-Instruct-v0.2-temp2 -cf Synatra-7B-Instruct-v0.2-8bpw-h8-exl2 -c train-00000-of-00001-21df739eb88d711e.parquet -l 4096 -b 8 -hb 8 -ss 4096 -m Synatra-7B-Instruct-v0.2-temp/measurement.json

Below this sentence is original model card

Synatra-7B-Instruct-v0.2

Made by StableFluffy

License

This model is strictly non-commercial (cc-by-nc-4.0) use only which takes priority over the LLAMA 2 COMMUNITY LICENSE AGREEMENT. The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included cc-by-nc-4.0 license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me.

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Model Details

Base Model
mistralai/Mistral-7B-Instruct-v0.1

Trained On
A6000 48GB * 8

TODO

• RP 기반 튜닝 모델 제작
• 데이터셋 정제
• 언어 이해능력 개선
• 상식 보완

Instruction format

In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

E.g.

text = "<s>[INST] 아이작 뉴턴의 업적을 알려줘. [/INST]"

Model Benchmark

Preparing...

Implementation Code

Since, chat_template already contains insturction format above. You can use the code below.

from transformers import AutoModelForCausalLM, AutoTokenizer

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

model = AutoModelForCausalLM.from_pretrained("maywell/Synatra-V0.1-7B")
tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-V0.1-7B")

messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
]

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])

If you run it on oobabooga your prompt would look like this.

[INST] 링컨에 대해서 알려줘. [/INST]

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

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