File size: 1,481 Bytes
98b1bf6 9c4e61c 60d07e4 9c4e61c 78d5272 9c4e61c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
---
license: cc-by-sa-4.0
---
# **Synatra-10.7B-v0.4π§**
# Join our discord
[Server Link](https://discord.gg/MrBt3PXdXc)
# **License**
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-sa-4.0** license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences.
# **Model Details**
**Base Model**
[upstage/SOLAR-10.7B-v1.0](https://huggingface.co./upstage/SOLAR-10.7B-v1.0)
**Trained On**
A100 80GB * 1
**Instruction format**
It follows **Alpaca** format.
# **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-10.7B-v0.4")
tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-10.7B-v0.4")
messages = [
{"role": "user", "content": "λ°λλλ μλ νμμμ΄μΌ?"},
]
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])
``` |