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---
base_model:
- unsloth/Qwen2.5-1.5B-Instruct
- unsloth/Qwen2.5-Coder-1.5B-Instruct
- unsloth/Qwen2.5-Math-1.5B-Instruct
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- unsloth/Qwen2.5-1.5B-Instruct
- unsloth/Qwen2.5-Coder-1.5B-Instruct
- unsloth/Qwen2.5-Math-1.5B-Instruct
---
> Note: This model is experimental and has not been tested for quality.
# Qwen2.5-Sci
Qwen2.5-Sci is a `mergekit` merge of the following models:
* [unsloth/Qwen2.5-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct)
* [unsloth/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct)
* [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct)
## 🧩 Configuration
```yaml
models:
- model: unsloth/Qwen2.5-1.5B-Instruct
parameters:
weight: 0.5
- model: unsloth/Qwen2.5-Coder-1.5B-Instruct
parameters:
weight: 0.3
- model: unsloth/Qwen2.5-Math-1.5B-Instruct
parameters:
weight: 0.2
merge_method: task_arithmetic
base_model: unsloth/Qwen2.5-1.5B-Instruct
parameters:
normalize: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "halbihn/Qwen2.5-Sci"
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"])
``` |