---
tags:
- text-generation
license: cc-by-nc-sa-4.0
language:
- ko
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: text-generation
---
# **DataVortexTL-1.1B-v0.1**
## **License**
[cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
## **Model Details**
### **Base Model**
[TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
### **Trained On**
H100 80GB 1ea
### **Instruction 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"
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexTL-1.1B-v0.1", device_map=device)
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexTL-1.1B-v0.1")
messages = [
{ "role": "user", "content": "대한민국의 수도는 어디야?" }
]
encoded = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
return_token_type_ids=False
).to(device)
decoded = model.generate(
input_ids=encoded,
temperature=0.2,
top_p=0.9,
repetition_penalty=1.2,
do_sample=True,
max_length=4096,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id
)
decoded = decoded[0][encoded.shape[1]:decoded[0].shape[-1]]
decoded_text = tokenizer.decode(decoded, skip_special_tokens=True)
print(decoded_text)
```
