File size: 1,941 Bytes
283bf41 178dd4c 283bf41 e3fb972 283bf41 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
---
tags:
- merge
- mergekit
- lazymergekit
- cognitivecomputations/dolphin-2_6-phi-2
- rhysjones/phi-2-orange
base_model:
- cognitivecomputations/dolphin-2_6-phi-2
- rhysjones/phi-2-orange
---
This is an experimental model made solely for the purpose of remerging it with its sibling model
# ReversePhiter
<img src="https://cdn-uploads.huggingface.co/production/uploads/6493317e9621f988db6c469c/IuZIzeFuIOWGjEbLJFuNq.jpeg" alt="ReversePhiter Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
ReversePhiter is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [cognitivecomputations/dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)
* [rhysjones/phi-2-orange](https://huggingface.co/rhysjones/phi-2-orange)
## 🧩 Configuration
```yaml
models:
- model: mixedbread-ai/phi-2
# no parameters necessary for base model
- model: cognitivecomputations/dolphin-2_6-phi-2
parameters:
density: 0.5
weight: 0.5
- model: rhysjones/phi-2-orange
parameters:
density: 0.5
weight: 0.3
merge_method: ties
base_model: mixedbread-ai/phi-2
parameters:
normalize: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Venkman42/ReversePhiter"
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"])
``` |