metadata
base_model:
- MLP-KTLim/llama-3-Korean-Bllossom-8B
- MLP-KTLim/llama-3-Korean-Bllossom-8B
- MLP-KTLim/llama-3-Korean-Bllossom-8B
- MLP-KTLim/llama-3-Korean-Bllossom-8B
- MLP-KTLim/llama-3-Korean-Bllossom-8B
tags:
- merge
- mergekit
- lazymergekit
- MLP-KTLim/llama-3-Korean-Bllossom-8B
Llama-3-Kor-Bllossom-12B
Llama-3-Kor-Bllossom-12B is a merge of the following models using LazyMergekit:
- MLP-KTLim/llama-3-Korean-Bllossom-8B
- MLP-KTLim/llama-3-Korean-Bllossom-8B
- MLP-KTLim/llama-3-Korean-Bllossom-8B
- MLP-KTLim/llama-3-Korean-Bllossom-8B
- MLP-KTLim/llama-3-Korean-Bllossom-8B
🧩 Configuration
slices:
- sources:
- model: MLP-KTLim/llama-3-Korean-Bllossom-8B
layer_range: [0,9]
- sources:
- model: MLP-KTLim/llama-3-Korean-Bllossom-8B
layer_range: [5,14]
- sources:
- model: MLP-KTLim/llama-3-Korean-Bllossom-8B
layer_range: [10,19]
- sources:
- model: MLP-KTLim/llama-3-Korean-Bllossom-8B
layer_range: [15,24]
- sources:
- model: MLP-KTLim/llama-3-Korean-Bllossom-8B
layer_range: [18,32]
merge_method: passthrough
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "sh2orc/Llama-3-Kor-Bllossom-12B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])