Update README.md

README.md

@@ -1,4 +1,5 @@
 ---
+license: apache-2.0
 base_model:
 - OpenPipe/mistral-ft-optimized-1218
 - mlabonne/NeuralHermes-2.5-Mistral-7B
@@ -6,18 +7,31 @@ tags:
 - merge
 - mergekit
 - lazymergekit
-- OpenPipe/mistral-ft-optimized-1218
-- mlabonne/NeuralHermes-2.5-Mistral-7B
+- mistral
+- optimized
 ---
+# NeuralPipe-7B-slerp (3B Parameters)
+
+This is an optimized merge of pre-trained language models created using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing), successfully reducing the original 7B models to approximately 3B parameters while maintaining core capabilities.
 
-# NeuralPipe-7B-slerp
+## About Me
+I'm David Soeiro-Vuong, a third-year Computer Science student working as an apprentice at TW3 Partners, a company specialized in Generative AI. Passionate about artificial intelligence and language models optimization, I focus on creating efficient model merges that balance performance and resource usage.
 
-NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+🔗 [Connect with me on LinkedIn](https://www.linkedin.com/in/david-soeiro-vuong-a28b582ba/)
+
+## Model Size Optimization
+The reduction from 7B to 3B parameters was achieved through:
+- Layer reduction from 32 to 12 layers
+- Conversion to bfloat16 format (half precision)
+- Selective layer range implementation
+- SLERP merge method optimization
+
+## Merge Details
+### Models Merged
 * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
 * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
 
-## 🧩 Configuration
-
+### Configuration
 ```yaml
 slices:
   - sources:
@@ -34,30 +48,4 @@ parameters:
     - filter: mlp
       value: [1, 0.5, 0.7, 0.3, 0]
     - value: 0.5
-dtype: bfloat16
-```
-
-## 💻 Usage
-
-```python
-!pip install -qU transformers accelerate
-
-from transformers import AutoTokenizer
-import transformers
-import torch
-
-model = "Davidsv/NeuralPipe-7B-slerp"
-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"])
-```
+dtype: bfloat16