Update README.md

README.md

@@ -70,7 +70,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 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).
+- **Context Length:** 32k 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