
Currently 2nd best model in ~7B category (actually closer to ~9B) on [Hugging Face Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)!

More information about making the model available here: 🔗[Don't stop DPOptimizing!](https://www.linkedin.com/pulse/dont-stop-dpoptimizing-jan-koco%2525C5%252584-mq4qf)

Author: Jan Kocoń &nbsp;&nbsp;&nbsp; 🔗[LinkedIn](https://www.linkedin.com/in/jankocon/) &nbsp;&nbsp;&nbsp; 🔗[Google Scholar](https://scholar.google.com/citations?user=pmQHb5IAAAAJ&hl=en&oi=ao) &nbsp;&nbsp;&nbsp; 🔗[ResearchGate](https://www.researchgate.net/profile/Jan-Kocon-2)

The "Neuronovo/neuronovo-9B-v0.2" model represents an advanced and fine-tuned version of a large language model, initially based on "CultriX/MistralTrix-v1." Several key characteristics and features of this model:

1. **Training Dataset**: The model is trained on a dataset named "Intel/orca_dpo_pairs," likely specialized for dialogue and interaction scenarios. This dataset is formatted to differentiate between system messages, user queries, chosen and rejected answers, indicating a focus on natural language understanding and response generation in conversational contexts.

2. **Tokenizer and Formatting**: It uses a tokenizer from the "CultriX/MistralTrix-v1" model, configured to pad tokens from the left and use the end-of-sequence token as the padding token. This suggests a focus on language generation tasks, particularly in dialogue systems.

3. **Low-Rank Adaptation (LoRA) Configuration**: The model incorporates a LoRA configuration with specific parameters like r=16, lora_alpha=16, and lora_dropout of 0.05. This is indicative of a fine-tuning process that aims to efficiently adapt the model to specific tasks by modifying only a small subset of the model's weights.

4. **Model Specifications for Fine-Tuning**: The model is fine-tuned using a custom setup, including a DPO (Data Parallel Optimization) Trainer. This highlights an emphasis on efficient training, possibly to optimize memory usage and computational resources, especially given the large scale of the model.

5. **Training Arguments and Strategies**: The training process uses specific strategies like gradient checkpointing, gradient accumulation, and a cosine learning rate scheduler. These methods are typically employed in training large models to manage resource utilization effectively.

6. **Performance and Output Capabilities**: Configured for causal language modeling, the model is capable of handling tasks that involve generating text or continuing dialogues, with a maximum prompt length of 1024 tokens and a maximum generation length of 1536 tokens. This suggests its aptitude for extended dialogues and complex language generation scenarios.

7. **Special Features and Efficiency**: The use of techniques like LoRA, DPO training, and specific fine-tuning methods indicates that the "Neuronovo/neuronovo-9B-v0.2" model is not only powerful in terms of language generation but also optimized for efficiency, particularly in terms of computational resource management.

In summary, "Neuronovo/neuronovo-9B-v0.2" is a highly specialized, efficient, and capable large language model, fine-tuned for complex language generation tasks in conversational AI, leveraging state-of-the-art techniques in model adaptation and efficient training methodologies.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/605f77e5575f3b3e6beb9067/c7AQrpmJVC6X-6cz3OHfc.png)

---
license: apache-2.0
language:
- en
library_name: transformers
---
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers"}
transformers safetensors mistral text-generation en license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us