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README.md

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+
+---
+
+tags:
+- merge
+- mergekit
+- lazymergekit
+- VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
+- mlabonne/ChimeraLlama-3-8B-v3
+base_model:
+- VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
+- mlabonne/ChimeraLlama-3-8B-v3
+license: mit
+pipeline_tag: text-generation
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/KingNish-Llama3-8b-GGUF
+This is quantized version of [KingNish/KingNish-Llama3-8b](https://huggingface.co/KingNish/KingNish-Llama3-8b) created using llama.cpp
+
+# Original Model Card
+
+
+# KingNish-Llama3-8b
+
+KingNish-Llama3-8b 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)
+
+## 🧩 Configuration
+
+```yaml
+models:
+  - model: nbeerbower/llama-3-gutenberg-8B
+    # No parameters necessary for base model
+  - model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
+    parameters:
+      density: 0.6
+      weight: 0.4
+  - model: mlabonne/ChimeraLlama-3-8B-v3
+    parameters:
+      density: 0.65
+      weight: 0.3
+merge_method: dare_ties
+base_model: nbeerbower/llama-3-gutenberg-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"
+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"])
+```