---
base_model:
- tiiuae/Falcon3-10B-Base
library_name: transformers
license: other
tags:
- mergekit
- merge

---
<img src="https://huggingface.co/arcee-train/Virtuoso-Lite/resolve/main/virtuoso-lite.jpg" alt="Virtuoso-Lite Logo" style="display: block; margin: 0 auto;" />

**Virtuoso-Lite (10B)** is our next-generation, 10-billion-parameter language model based on the Llama-3 architecture. It is distilled from Deepseek-v3 using ~1.1B tokens/logits, allowing it to achieve robust performance at a significantly reduced parameter count compared to larger models. Despite its compact size, Virtuoso-Lite excels in a variety of tasks, demonstrating advanced reasoning, code generation, and mathematical problem-solving capabilities.

## Note
This is the pre-distilled version of [Virtuoso-Lite](https://huggingface.co/arcee-ai/Virtuoso-Lite), a standard SFT+RLHF merge training run used to validate the model before proceeding with the additional training steps outlined below.
### GGUF
Quantizations available [here](https://huggingface.co/arcee-ai/Virtuoso-Lite-GGUF)

### Model Details
- **Architecture Base:** Falcon-10B (based on Llama-3)
- **Parameter Count:** 10B
- **Tokenizer:**  
  - Initially integrated with Deepseek-v3 tokenizer for logit extraction.  
  - Final alignment uses the Llama-3 tokenizer, with specialized “tokenizer surgery” for cross-architecture compatibility.
- **Distillation Data:**  
  - ~1.1B tokens/logits from Deepseek-v3’s training data.
  - Logit-level distillation using a proprietary “fusion merging” approach for maximum fidelity.
- **License:** [falcon-llm-license](https://falconllm.tii.ae/falcon-terms-and-conditions.html)

### Background on Deepseek Distillation
Deepseek-v3 serves as the teacher model, from which we capture logits across billions of tokens. Rather than standard supervised fine-tuning, Virtuoso-Lite applies a full logit-level replication to preserve the most crucial insights from the teacher. This approach enables:
- Strong performance on technical/scientific queries
- Enhanced code generation and debugging
- Improved consistency in math-intensive tasks

### Intended Use Cases
- **Chatbots & Virtual Assistants**  
- **Lightweight Enterprise Data Analysis**  
- **Research Prototypes & Proofs of Concept**  
- **STEM Educational Tools (where smaller footprint is advantageous)**

### Evaluations
<img src="https://huggingface.co/arcee-train/Virtuoso-Lite/resolve/main/Benchmarks.png" alt="Virtuoso-Lite Logo" style="display: block; margin: 0 auto;" />


### How to Use
Below is a sample code snippet using `transformers`:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "arcee-ai/virtuoso-lite"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Provide a concise summary of quantum entanglement."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Training & Fine-Tuning
- **Initial Training:** Began with Falcon-10B, optimized for large-scale text ingestion.  
- **Distillation & Merging:**  
  - Trained on ~1.1B tokens/logits from Deepseek-v3.  
  - Employed “fusion merging” to capture detailed teacher insights.  
  - Final step included DPO to enhance alignment and mitigate hallucinations.
- **Future Developments:** We plan to incorporate additional R1 distillations to further improve specialized performance and reduce model footprint.

### Performance
Virtuoso-Lite demonstrates strong results across multiple benchmarks (e.g., BBH, MMLU-PRO, MATH), often standing its ground against models with higher parameter counts. This efficiency is largely credited to logit-level distillation, which compresses the teacher model’s capabilities into a more parameter-friendly package.

### Limitations
- **Context Length:** 128k Tokens (may vary depending on the final tokenizer settings and system resources).
- **Knowledge Cut-off:** Training data may not reflect the latest events or developments beyond June 2024.

### Ethical Considerations
- **Content Generation Risks:** Like any language model, Virtuoso-Lite can generate potentially harmful or biased content if prompted in certain ways.
- 
### License
**Virtuoso-Lite (10B)** is released under the [falcon-llm-license License](https://falconllm.tii.ae/falcon-terms-and-conditions.html). You are free to use, modify, and distribute this model in both commercial and non-commercial applications, subject to the terms and conditions of the license.

If you have questions or would like to share your experiences using Virtuoso-Lite (10B), please connect with us on social media. We’re excited to see what you build—and how this model helps you innovate!