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tags:
- merge
- mergekit
- lazymergekit
- codellama/CodeLlama-7b-Instruct-hf
base_model:
- codellama/CodeLlama-7b-Instruct-hf
---
# CodeLlemur-2B-Instruct
CodeLlemur-2B-Instruct is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf)
## 🧩 Configuration
```yaml
dtype: bfloat16
merge_method: linear
slices:
- sources:
- layer_range: [0, 32] # Assuming the first half of the model is more general and can be reduced more
model: codellama/CodeLlama-7b-Instruct-hf
parameters:
weight: 0.25 # Reduce the weight of the first half to make room for the second half
- layer_range: [0, 32] # Assuming the second half of the model is more specialized and can be reduced less
model: codellama/CodeLlama-7b-Instruct-hf
parameters:
weight: 0.25 # Maintain the weight of the second half
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "JoPmt/CodeLlemur-2B-Instruct"
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"])
``` |