Synatra-7B-v0.3-RP / README.md
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Adding Evaluation Results (#2)
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---
language:
- ko
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-4.0
---
# **Synatra-7B-v0.3-RP🐧**
![Synatra-7B-v0.3-RP](./Synatra.png)
## Support Me
μ‹œλ‚˜νŠΈλΌλŠ” 개인 ν”„λ‘œμ νŠΈλ‘œ, 1인의 μžμ›μœΌλ‘œ 개발되고 μžˆμŠ΅λ‹ˆλ‹€. λͺ¨λΈμ΄ λ§ˆμŒμ— λ“œμ…¨λ‹€λ©΄ μ•½κ°„μ˜ 연ꡬ비 지원은 μ–΄λ–¨κΉŒμš”?
[<img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy me a Coffee" width="217" height="50">](https://www.buymeacoffee.com/mwell)
Wanna be a sponser? Contact me on Telegram **AlzarTakkarsen**
# **License**
This model is strictly [*non-commercial*](https://creativecommons.org/licenses/by-nc/4.0/) (**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](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
**Trained On**
A6000 48GB * 8
**Instruction format**
It follows [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) format.
**TODO**
- ~~``RP 기반 νŠœλ‹ λͺ¨λΈ μ œμž‘``~~ βœ…
- ~~``데이터셋 μ •μ œ``~~ βœ…
- μ–Έμ–΄ 이해λŠ₯λ ₯ κ°œμ„ 
- ~~``상식 보완``~~ βœ…
- ν† ν¬λ‚˜μ΄μ € λ³€κ²½
# **Model Benchmark**
## Ko-LLM-Leaderboard
On Benchmarking...
# **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-7B-v0.3-RP")
tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-7B-v0.3-RP")
messages = [
{"role": "user", "content": "λ°”λ‚˜λ‚˜λŠ” μ›λž˜ ν•˜μ–€μƒ‰μ΄μ•Ό?"},
]
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])
```
# Why It's benchmark score is lower than preview version?
**Apparently**, Preview model uses Alpaca Style prompt which has no pre-fix. But ChatML do.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Synatra-7B-v0.3-RP)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 57.38 |
| ARC (25-shot) | 62.2 |
| HellaSwag (10-shot) | 82.29 |
| MMLU (5-shot) | 60.8 |
| TruthfulQA (0-shot) | 52.64 |
| Winogrande (5-shot) | 76.48 |
| GSM8K (5-shot) | 21.15 |
| DROP (3-shot) | 46.06 |