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---
tags:
- merge
- mergekit
- lazymergekit
- VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
- mlabonne/ChimeraLlama-3-8B-v3
- MaziyarPanahi/Llama-3-8B-Instruct-v0.4
base_model:
- VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
- mlabonne/ChimeraLlama-3-8B-v3
- MaziyarPanahi/Llama-3-8B-Instruct-v0.4
---
[](https://hf.co/QuantFactory)
# QuantFactory/KingNish-Llama3-8b-v0.2-GGUF
This is quantized version of [KingNish/KingNish-Llama3-8b-v0.2](https://huggingface.co/KingNish/KingNish-Llama3-8b-v0.2) created using llama.cpp
# Original Model Card
# KingNish-Llama3-8b-v0.2
KingNish-Llama3-8b-v0.2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct](https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct)
* [mlabonne/ChimeraLlama-3-8B-v3](https://huggingface.co/mlabonne/ChimeraLlama-3-8B-v3)
* [MaziyarPanahi/Llama-3-8B-Instruct-v0.4](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.4)
## 🧩 Configuration
```yaml
models:
- model: KingNish/KingNish-Llama3-8b
# No parameters necessary for base model
- model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
parameters:
density: 0.7
weight: 0.5
- model: mlabonne/ChimeraLlama-3-8B-v3
parameters:
density: 0.65
weight: 0.25
- model: MaziyarPanahi/Llama-3-8B-Instruct-v0.4
parameters:
density: 0.55
weight: 0.1
merge_method: dare_ties
base_model: KingNish/KingNish-Llama3-8b
parameters:
int8_mask: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "KingNish/KingNish-Llama3-8b-v0.2"
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"])
```
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