Update README.md

README.md

@@ -5,7 +5,23 @@ language:
 library_name: transformers
 pipeline_tag: text-generation
 ---
-Details soon.
+The model described by the provided code, named "Neuronovo/neuronovo-7B-v0.1," is a sophisticated and fine-tuned version of a large language model, originally based on the "teknium/OpenHermes-2.5-Mistral-7B." This model exhibits several distinct characteristics and functionalities as derived from the code snippet:
+
+1. **Dataset and Preprocessing**: It is trained on a dataset named "Intel/orca_dpo_pairs," which is likely a specialized dataset for dialogue systems. The data is preprocessed to format dialogues, with specific attention to system messages, user queries, chosen answers, and rejected answers.
+
+2. **Tokenizer**: The model utilizes a tokenizer from the original "OpenHermes-2.5-Mistral-7B" model. This tokenizer is configured to have the end-of-sequence token as the padding token and pads from the left, indicating a particular focus on language generation tasks.
+
+3. **LoRA Configuration**: The model employs a LoRA (Low-Rank Adaptation) configuration with specific parameters (r=16, lora_alpha=16, etc.) and targets multiple modules within the transformer architecture. This suggests an approach focused on efficient fine-tuning and adaptation of the model while preserving the majority of the pre-trained weights.
+
+4. **Fine-Tuning Specifications**: The model is fine-tuned using a custom training setup, including a special DPO (Data Parallel Optimization) Trainer. This indicates an advanced fine-tuning process that likely emphasizes both efficiency and effectiveness, possibly with a focus on parallel processing and optimization.
+
+5. **Training Arguments**: The training uses specific arguments like a cosine learning rate scheduler, paged AdamW optimizer, and training in 4-bit precision (indicating a focus on memory efficiency). It also employs gradient checkpointing and accumulation steps, which are typical in training large models efficiently.
+
+6. **Performance and Output**: The model is configured for causal language modeling (indicative of generating text or continuing dialogues), with a maximum prompt length of 1024 and maximum generation length of 1536 tokens. This setup suggests its capability for handling extended dialogues or text generation tasks.
+
+7. **Special Features**: The use of LoRA, DPO training, and specific fine-tuning methods highlight the model's advanced capabilities in adapting large-scale language models to specific tasks or datasets while maintaining computational efficiency.
+
+In summary, "Neuronovo/neuronovo-7B-v0.1" is a highly specialized, efficient, and capable large language model fine-tuned for advanced language generation tasks, particularly in the context of dialogues or interactions, leveraging cutting-edge techniques in NLP model adaptation and training.
 
 LinkedIn: https://www.linkedin.com/in/jankocon/