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-temp3 -cf Synatra-7B-Instruct-v0.2-6bpw-h8-exl2 -c train-00000-of-00001-21df739eb88d711e.parquet -l 4096 -b 6 -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.

To Contact, Join Discord Server

Model Details

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

Trained On
A6000 48GB * 8

TODO

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