Update CQ branding to Coded Quantization and ThousandML

README.md

@@ -16,17 +16,17 @@ pipeline_tag: text-generation
 
 # Qwen3-Coder-Next-CQ-4bit
 
-> **80B-parameter Mixture-of-Experts coding model · 3B activated parameters · Thousandml Quant (CQ) 4-bit**
+> **80B-parameter Mixture-of-Experts coding model · 3B activated parameters · Coded Quantization (CQ) 4-bit**
 >
 > **Note on Hugging Face's model-size display:** Hugging Face may report a smaller stored parameter count for this repository because CQ stores the weights in a packed 4-bit representation. The underlying model is **Qwen3-Coder-Next**, with approximately **80B total parameters and 3B activated parameters per token**. The smaller number reflects the packed storage tensors counted by the Hub, not the parameter count of the original model architecture.
 
-**Qwen3-Coder-Next quantized to 4-bit with Thousandml Quant (CQ).**
+**Qwen3-Coder-Next quantized to 4-bit with Coded Quantization (CQ).**
 
-**Thousandml Quant:** https://thousandml.com
+**ThousandML:** https://thousandml.com
 
 CQ is a **calibration-free 4-bit quantization method** designed to preserve the behavior of the original model without requiring a calibration dataset. Because CQ does not optimize the quantization around a fixed calibration corpus, it is designed to generalize beyond calibration-specific data and workloads.
 
-This repository contains **Qwen3-Coder-Next** quantized with **Thousandml Quant (CQ) 4-bit**.
+This repository contains **Qwen3-Coder-Next** quantized with **Coded Quantization (CQ) 4-bit**.
 
 ## Why CQ?
 
@@ -45,7 +45,7 @@ This avoids tying the quantization procedure to a particular calibration corpus
 | **Base model** | `Qwen/Qwen3-Coder-Next` |
 | **Original model parameters** | **~80B total / ~3B activated** |
 | **Model type** | Mixture-of-Experts coding model |
-| **Quantization** | **Thousandml Quant (CQ) 4-bit** |
+| **Quantization** | **Coded Quantization (CQ) 4-bit** |
 | **Calibration** | **None — calibration-free** |
 | **Runtime** | vLLM + CQ custom kernels |
 | **Quantization name** | `cq` |
@@ -143,11 +143,11 @@ On WSL2, combine it with:
 -e VLLM_WSL2_ENABLE_PIN_MEMORY=1
 ```
 
-## About Thousandml Quant (CQ)
+## About Coded Quantization (CQ)
 
 Learn more at **https://thousandml.com**.
 
-**Thousandml Quant (CQ)** is a calibration-free quantization approach for efficient LLM inference.
+**Coded Quantization (CQ)** is a calibration-free quantization approach for efficient LLM inference.
 
 Traditional post-training quantization methods can depend on a calibration dataset to estimate quantization parameters. The resulting quantization can therefore be influenced by the distribution and composition of that calibration data.