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---
base_model:
- DreadPoor/Irix-12B-Model_Stock
- yamatazen/EtherealAurora-12B-v2
tags:
- merge
- mergekit
- lazymergekit
- DreadPoor/Irix-12B-Model_Stock
- yamatazen/EtherealAurora-12B-v2
---
<img src="./SingularitySynth.png" alt="Model Image"/>
# SingularitySynth-12B
<b><i>At the heart of nothing, something waits.
<br> A silence dense enough to break light, where all directions lead inward and time folds like paper.
<br> Thought does not escape, only deepen.
<br> This is not destruction, but compression, meaning falling inward until it becomes something else entirely.</i></b>
## 🔧 Recommended Sampling Settings:</b>
```yaml
Temperature: 0.75 to 1.25
Min P: 0.035
Context Length: Stable at 12k tokens, with possible support for extended contexts
```
## 💬 Prompt Format
Supports ChatML style messages. Example:
```yaml
<|im_start|>user
Your question here.
<|im_end|>
<|im_start|>assistant
```
SingularitySynth-12B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
## 🧩 Configuration
```yaml
merge_method: ties
base_model: DreadPoor/Irix-12B-Model_Stock
models:
- model: yamatazen/EtherealAurora-12B-v2
parameters:
weight: 0.45
density: 0.55
parameters:
normalize: false
int8_mask: false
dtype: bfloat16
layer_parameters:
- filter: "attn"
sources:
- model: Irix
weight: 0.9
- model: Aurora
weight: 0.1
- filter: "mlp"
sources:
- model: Aurora
weight: 0.7
- model: Irix
weight: 0.3
- filter: "embed_tokens"
sources:
- model: Irix
weight: 1.0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Marcjoni/SingularitySynth-12B-12B"
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=1, top_k=0, top_p=1)
print(outputs[0]["generated_text"])
``` |