Update README.md

README.md

@@ -114,6 +114,42 @@ model-index:
 This model was converted to GGUF format from [`HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407`](https://huggingface.co/HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407) for more details on the model.
 
+---
+Model details:
+-
+This model is a fine-tuned version of mistralai/Mistral-Nemo-Instruct-2407, specifically optimized to generate more human-like and conversational responses.
+
+The fine-tuning process employed both Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO) to enhance natural language understanding, conversational coherence, and emotional intelligence in interactions.
+
+The proccess of creating this models is detailed in the research paper “Enhancing Human-Like Responses in Large Language Models”.
+
+🛠️ Training Configuration
+
+    Base Model: Mistral-Nemo-Instruct-2407
+    Framework: Axolotl v0.4.1
+    Hardware: 2x NVIDIA A100 (80 GB) GPUs
+    Training Time: ~3 hours 40 minutes
+    Dataset: Synthetic dataset with ≈11,000 samples across 256 diverse topics
+
+
+💬 Prompt Template
+
+You can use Mistral-Nemo prompt template while using the model:
+
+Mistral-Nemo
+
+<s>[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today?</s> [INST] I'd like to show off how chat templating works! [/INST]
+
+This prompt template is available as a chat template, which means you can format messages using the tokenizer.apply_chat_template() method:
+
+messages = [
+    {"role": "system", "content": "You are helpful AI asistant."},
+    {"role": "user", "content": "Hello!"}
+]
+gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
+model.generate(**gen_input)
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)