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---
tags:
- merge
- mergekit
- lazymergekit
- winninghealth/WiNGPT2-Llama-3-8B-Base
- johnsnowlabs/JSL-MedLlama-3-8B-v1.0
base_model:
- winninghealth/WiNGPT2-Llama-3-8B-Base
- johnsnowlabs/JSL-MedLlama-3-8B-v1.0
license: llama3
language:
- zh
- en
- fr
---
# llama3-8B-slerp-med-chinese
llama3-8B-slerp-med-chinese is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [winninghealth/WiNGPT2-Llama-3-8B-Base](https://huggingface.co/winninghealth/WiNGPT2-Llama-3-8B-Base)
* [johnsnowlabs/JSL-MedLlama-3-8B-v1.0](https://huggingface.co/johnsnowlabs/JSL-MedLlama-3-8B-v1.0)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: winninghealth/WiNGPT2-Llama-3-8B-Base
layer_range: [0,32]
- model: johnsnowlabs/JSL-MedLlama-3-8B-v1.0
layer_range: [0,32]
merge_method: slerp
base_model: winninghealth/WiNGPT2-Llama-3-8B-Base
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.5, 0.5, 1]
- filter: mlp
value: [1, 0.5, 0.5, 0.5, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "shanchen/llama3-8B-slerp-med-chinese"
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"])
``` |