Card accuracy pass 2: remove unmeasured speed claims, honest brand labels

README.md

@@ -40,7 +40,7 @@ pipeline_tag: text-generation
 
 **8-bit MLX weight-quantized [Leanstral-2603](https://huggingface.co/mistralai/Leanstral-2603) with [RotorQuant](https://github.com/scrya-com/rotorquant) KV-cache quantization for Lean 4 formal proof generation on Apple Silicon.**
 
-Leanstral is the first open-source AI agent purpose-built for Lean 4 formal proofs -- generating both executable code and machine-checkable mathematical proofs. This variant combines **dual compression**: 8-bit MLX weight quantization for reduced model size plus the legacy RotorQuant KV-cache fork (superseded by upstream llama.cpp KV options) for efficient long-context inference with faster prefill and decode.
+Leanstral is the first open-source AI agent purpose-built for Lean 4 formal proofs -- generating both executable code and machine-checkable mathematical proofs. This variant combines **dual compression**: 8-bit MLX weight quantization for reduced model size plus the legacy RotorQuant KV-cache fork (superseded by upstream llama.cpp KV options) for efficient long-context inference with KV-cache handling.
 
 Approximate model size: **~120 GB**
 
@@ -90,7 +90,7 @@ upstream options described above (`-ctk/-ctv q8_0`, `OLLAMA_KV_CACHE_TYPE`).
 | Method | Prefill Speed | Decode Speed | Memory Savings | Reference |
 |---|---|---|---|---|
 | **TurboQuant** | Baseline | Baseline | High | [arXiv: 2504.19874](https://arxiv.org/abs/2504.19874) |
-| **RotorQuant** | 5.3x faster | 28% faster | High | [GitHub](https://github.com/scrya-com/rotorquant) |
+| **RotorQuant** | different | 28% faster | High | [GitHub](https://github.com/scrya-com/rotorquant) |
 
 ## Memory Estimates