Create README.md

README.md

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+---
+license: apache-2.0
+---
+DPO of MonaTrix-v4 with this dataset: https://huggingface.co/datasets/CultriX/dpo-mix-ambrosia-cleaned
+
+---
+tags:
+- merge
+- mergekit
+- lazymergekit
+- Kukedlc/NeuralMaxime-7B-slerp
+- eren23/ogno-monarch-jaskier-merge-7b
+- eren23/dpo-binarized-NeutrixOmnibe-7B
+base_model:
+- Kukedlc/NeuralMaxime-7B-slerp
+- eren23/ogno-monarch-jaskier-merge-7b
+- eren23/dpo-binarized-NeutrixOmnibe-7B
+license: apache-2.0
+---
+
+# MonaTrix-v4
+
+MonaTrix-v4 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [Kukedlc/NeuralMaxime-7B-slerp](https://huggingface.co/Kukedlc/NeuralMaxime-7B-slerp)
+* [eren23/ogno-monarch-jaskier-merge-7b](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b)
+* [eren23/dpo-binarized-NeutrixOmnibe-7B](https://huggingface.co/eren23/dpo-binarized-NeutrixOmnibe-7B)
+
+## 🧩 Configuration
+
+```yaml
+models:
+  - model: mistralai/Mistral-7B-v0.1
+    # No parameters necessary for base model
+  - model: Kukedlc/NeuralMaxime-7B-slerp
+    #Emphasize the beginning of Vicuna format models
+    parameters:
+      weight: 0.36
+      density: 0.65
+  - model: eren23/ogno-monarch-jaskier-merge-7b
+    parameters:
+      weight: 0.34
+      density: 0.6
+  # Vicuna format
+  - model: eren23/dpo-binarized-NeutrixOmnibe-7B
+    parameters:
+      weight: 0.3
+      density: 0.6
+
+merge_method: dare_ties
+base_model: mistralai/Mistral-7B-v0.1
+parameters:
+  int8_mask: true
+dtype: bfloat16
+random_seed: 0
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "CultriX/MonaTrix-v4"
+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"])
+```