Update README.md

README.md

@@ -1,3 +1,75 @@
 ---
 license: apache-2.0
 ---
+---
+tags:
+- merge
+- mergekit
+- '#dpo'
+- MaximeLabonne
+- '#mergeofmerge'
+base_model:
+- CultriX/NeuralTrix-7B-dpo
+- paulml/OmniBeagleSquaredMBX-v3-7B-v2
+
+license: apache-2.0
+---
+# This model was merged, trained, and so on, thanks to the knowledge I gained from reading Maxime Labonne's course. Special thanks to him! 
+[Labonne LLM Course](https://github.com/mlabonne/llm-course)
+
+![NeuTrixOmniBe](https://raw.githubusercontent.com/kukedlc87/imagenes/main/DALL%C2%B7E%202023-12-29%2002.13.09%20-%20A%20robot%20with%20a%20unique%20design%20where%20its%20face%20is%20a%20screen%20displaying%20binary%20code.%20The%20robot's%20body%20is%20sleek%20and%20modern%2C%20with%20a%20metallic%20finish%20that%20refl.png)
+
+# NeuTrixOmniBe-DPO
+
+NeuTrix7000-7b-DPO is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+
+## 🧩 Configuration
+
+```yaml
+MODEL_NAME = "NeuTrix7000-7b-DPO"
+yaml_config = """
+slices:
+  - sources:
+      - model: CultriX/NeuralTrix-7B-dpo
+        layer_range: [0, 32]
+      - model: paulml/OmniBeagleSquaredMBX-v3-7B-v2
+        layer_range: [0, 32]
+merge_method: slerp
+base_model: CultriX/NeuralTrix-7B-dpo
+parameters:
+  t:
+    - filter: self_attn
+      value: [0, 0.5, 0.3, 0.7, 1]
+    - filter: mlp
+      value: [1, 0.5, 0.7, 0.3, 0]
+    - value: 0.5
+dtype: bfloat16
+"""
+```
+
+It was then trained with DPO using: 
+* Intel/orca_dpo_pairs
+
+
+
+
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+from transformers import AutoTokenizer
+import transformers
+import torch
+model = "Kukedlc/NeuTrix7000-7b-DPO"
+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=128, do_sample=True, temperature=0.5, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])