arcee-ai/Virtuoso-Lite-GGUF

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.

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

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

How to Use

Below is a sample code snippet using transformers:

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. 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!