Instructions to use maywell/Synatra-7B-Instruct-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maywell/Synatra-7B-Instruct-v0.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="maywell/Synatra-7B-Instruct-v0.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-7B-Instruct-v0.2")
model = AutoModelForCausalLM.from_pretrained("maywell/Synatra-7B-Instruct-v0.2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use maywell/Synatra-7B-Instruct-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maywell/Synatra-7B-Instruct-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maywell/Synatra-7B-Instruct-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/maywell/Synatra-7B-Instruct-v0.2

SGLang

How to use maywell/Synatra-7B-Instruct-v0.2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "maywell/Synatra-7B-Instruct-v0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maywell/Synatra-7B-Instruct-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "maywell/Synatra-7B-Instruct-v0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maywell/Synatra-7B-Instruct-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use maywell/Synatra-7B-Instruct-v0.2 with Docker Model Runner:
```
docker model run hf.co/maywell/Synatra-7B-Instruct-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Synatra-7B-Instruct-v0.2

Made by StableFluffy

Contact (Do not Contact for personal things.) Discord : is.maywell Telegram : AlzarTakkarsen

License

This model is strictly non-commercial (cc-by-nc-4.0) use only. 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.

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
토크나이저 변경

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

Ko-LLM-Leaderboard

Model	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2	Avg
kyujinpy/KoT-platypus2-13B(No.1 at 2023/10/12)	43.69	53.05	42.29	43.34	65.38	49.55
Synatra-V0.1-7B-Instruct	41.72	49.28	43.27	43.75	39.32	43.47
Synatra-7B-Instruct-v0.2	41.81	49.35	43.99	45.77	42.96	44.78

MMLU에서는 우세하나 Ko-CommonGen V2 에서 크게 약한 모습을 보임.

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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