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---
base_model:
- cgato/L3-TheSpice-8b-v0.8.3
- Sao10K/L3-8B-Stheno-v3.1
- Nitral-AI/Hathor_Stable-v0.2-L3-8B
- aifeifei798/llama3-8B-DarkIdol-1.0
- ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B
- ResplendentAI/Nymph_8B
tags:
- merge
- mergekit
- lazymergekit
- cgato/L3-TheSpice-8b-v0.8.3
- Sao10K/L3-8B-Stheno-v3.1
- Nitral-AI/Hathor_Stable-v0.2-L3-8B
- aifeifei798/llama3-8B-DarkIdol-1.0
- ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B
- ResplendentAI/Nymph_8B
---
# L3-Uncen-Merger-Omelette-RP-v0.2-8B
L3-Uncen-Merger-Omelette-RP-v0.2-8B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [cgato/L3-TheSpice-8b-v0.8.3](https://huggingface.co/cgato/L3-TheSpice-8b-v0.8.3)
* [Sao10K/L3-8B-Stheno-v3.1](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.1)
* [Nitral-AI/Hathor_Stable-v0.2-L3-8B](https://huggingface.co/Nitral-AI/Hathor_Stable-v0.2-L3-8B)
* [aifeifei798/llama3-8B-DarkIdol-1.0](https://huggingface.co/aifeifei798/llama3-8B-DarkIdol-1.0)
* [ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B)
* [ResplendentAI/Nymph_8B](https://huggingface.co/ResplendentAI/Nymph_8B)
## 🧩 Configuration
```yaml
models:
- model: Casual-Autopsy/Omelette-2
- model: cgato/L3-TheSpice-8b-v0.8.3
parameters:
weight: 0.01
- model: Sao10K/L3-8B-Stheno-v3.1
parameters:
weight: 0.01
- model: Nitral-AI/Hathor_Stable-v0.2-L3-8B
parameters:
weight: 0.01
- model: aifeifei798/llama3-8B-DarkIdol-1.0
parameters:
weight: 0.02
- model: ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B
parameters:
weight: 0.025
- model: ResplendentAI/Nymph_8B
parameters:
weight: 0.025
merge_method: task_arithmetic
base_model: Casual-Autopsy/Omelette-2
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-v0.2-8B"
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"])
``` |