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---
license: apache-2.0
tags:
- mergekit
- mistralai/Mistral-7B-Instruct-v0.2
- mistralai/Mistral-7B-Instruct-v0.2
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- mistralai/Mistral-7B-Instruct-v0.2
---
# Aurora-10.7b_Base
Aurora-10.7b_Base is a merge of the following models: to create a 10.7b base model that can be trained.
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
## Merged Evals: (Has Not Been Finetuned)
Aurora-10.7b_Base
* Avg: 63.98
* ARC: 62.88
* HellaSwag: 83.99
* MMLU: 60.24
* T-QA: 67.84
* Winogrande: 76.4
* GSM8K: 32.52
## (OG)Donated Evals:
Mistral-7b-v0.2
* Avg: 65.71
* ARC: 63.14
* HellaSwag: 84.88
* MMLU: 60.78
* T-QA: 68.26
* Winogrande: 77.19
* GSM8K: 40.03
## 🧩 Configuration
```
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 24]
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Steelskull/Aurora_base_test"
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"])
``` |