---
language:
- ko

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

# **Synatra-V0.1-7B**  

## Model Details

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

**Trained On**  
A6000 48GB * 8

## 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.
Plus, It is strongly recommended to add a space at the end of the prompt.

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])
```

> Readme format: [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b)

---