---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP
- VAGOsolutions/SauerkrautLM-SOLAR-Instruct
---

# SOLAR-10.7B-Instruct-ties

SOLAR-10.7B-Instruct-ties is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP](https://huggingface.co/kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP)
* [VAGOsolutions/SauerkrautLM-SOLAR-Instruct](https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct)

## 🧩 Configuration

```yaml
models:
  - model: upstage/SOLAR-10.7B-Instruct-v1.0
    # no parameters necessary for base model
  - model: kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP
    parameters:
      density: 0.5
      weight: 0.5
  - model: VAGOsolutions/SauerkrautLM-SOLAR-Instruct
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
parameters:
  normalize: true
dtype: float16
```
## 💻 Example Python Code

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "nfaheem/SOLAR-10.7B-Instruct-ties"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''{prompt}
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])
```
## 📋 Summary Eval:

| Average |  ARC  | HellaSwag |  MMLU  | TruthfulQA | Winogrande | GSM8K |
|---------|-------|-----------|--------|------------|------------|-------|
|  74.24  | 70.9  |   88.58   |  66.34 |    71.88   |    83.5    | 64.06 |



## 📈 Huggingface Leaderboard


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b6cc785fd617abdfec6bed/yFGDhunmwne1T6roFVDKg.png)