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---
base_model:
- HuggingFaceTB/SmolLM-1.7B-Instruct
- HuggingFaceTB/SmolLM-1.7B
tags:
- merge
- mergekit
- lazymergekit
- HuggingFaceTB/SmolLM-1.7B-Instruct
- HuggingFaceTB/SmolLM-1.7B
---
# smollm-dareties
smollm-dareties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [HuggingFaceTB/SmolLM-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-1.7B-Instruct)
* [HuggingFaceTB/SmolLM-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM-1.7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- layer_range: [0, 24]
model: HuggingFaceTB/SmolLM-1.7B-Instruct
parameters:
density: [1, 0.7, 0.1]
weight: 1.0
- layer_range: [0, 24]
model: HuggingFaceTB/SmolLM-1.7B
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: dare_ties
base_model: HuggingFaceTB/SmolLM-1.7B-Instruct
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "aipib/smollm-dareties"
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"])
``` |