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---
tags:
- merge
- mergekit
- lazymergekit
- shanchen/llama3-8B-slerp-med-chinese
- shenzhi-wang/Llama3-8B-Chinese-Chat
base_model:
- shanchen/llama3-8B-slerp-med-chinese
- shenzhi-wang/Llama3-8B-Chinese-Chat
license: llama3
language:
- zh
- en
---
# llama3-8B-slerp-biomed-chat-chinese
llama3-8B-slerp-biomed-chat-chinese is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [shanchen/llama3-8B-slerp-med-chinese](https://huggingface.co/shanchen/llama3-8B-slerp-med-chinese)
* [shenzhi-wang/Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: shanchen/llama3-8B-slerp-med-chinese
layer_range: [0,32]
- model: shenzhi-wang/Llama3-8B-Chinese-Chat
layer_range: [0,32]
merge_method: slerp
base_model: shenzhi-wang/Llama3-8B-Chinese-Chat
parameters:
t:
- filter: self_attn
value: [0.3, 0.5, 0.5, 0.7, 1]
- filter: mlp
value: [1, 0.7, 0.5, 0.5, 0.3]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "shanchen/llama3-8B-slerp-biomed-chat-chinese"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, torch_dtype="auto", device_map="auto"
)
messages = [
{"role": "user", "content": "Can you speak Japanese?"},
]
input_ids = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
input_ids,
max_new_tokens=192 max#8192,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
``` |