README.md

---
language:
- ko

library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-4.0
---

# **Synatra-7B-Instruct-v0.2**  

Made by StableFluffy

## License

This model is strictly [*non-commercial*](https://creativecommons.org/licenses/by-nc/4.0/) (**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](https://discord.gg/HTUBtvjUZa)

## Model Details
**Base Model**  
[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/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**

Preparing...

# **Implementation Code**

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

```python
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](https://huggingface.co/beomi/llama-2-ko-7b)

---