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---
tags:
- merge
- mergekit
- lazymergekit
- fhai50032/BeagleLake-7B-Toxic
- Arc53/docsgpt-7b-mistral
base_model:
- fhai50032/BeagleLake-7B-Toxic
- Arc53/docsgpt-7b-mistral
license: apache-2.0
---
# Toctabledog7b
Toctabledog7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [fhai50032/BeagleLake-7B-Toxic](https://huggingface.co/fhai50032/BeagleLake-7B-Toxic)
* [Arc53/docsgpt-7b-mistral](https://huggingface.co/Arc53/docsgpt-7b-mistral)
The idea is to get an smart and efficient RAG happy assistant that won't judge you while for what it finds while searching through your lemon collection.
This merge wasn't made to discover facts but ideas.
It seems okay, but take the results it finds with a pinch of salt.
Cursory testing with REOR (https://github.com/reorproject/reor) seems positive. YMMV
## 🧩 Configuration
```yaml
slices:
- sources:
- model: fhai50032/BeagleLake-7B-Toxic
layer_range: [0, 32]
- model: Arc53/docsgpt-7b-mistral
layer_range: [0, 32]
merge_method: slerp
base_model: fhai50032/BeagleLake-7B-Toxic
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "UniLLMer/Toctabledog7b"
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"])
``` |