fix: bump docs

docs/source/reference/launcher.md

@@ -59,8 +59,6 @@ Options:
           
           Marlin kernels will be used automatically for GPTQ/AWQ models.
 
-          [env: QUANTIZE=]
-
           Possible values:
           - awq:                4 bit quantization. Requires a specific AWQ quantized model: <https://hf.co/models?search=awq>. Should replace GPTQ models wherever possible because of the better latency
           - compressed-tensors: Compressed tensors, which can be a mixture of different quantization methods
@@ -73,6 +71,8 @@ Options:
           - bitsandbytes-fp4:   Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
           - fp8:                [FP8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/) (e4m3) works on H100 and above This dtype has native ops should be the fastest if available. This is currently not the fastest because of local unpacking + padding to satisfy matrix multiplication limitations
           
+          [env: QUANTIZE=]
+
 ```
 ## SPECULATE
 ```shell
@@ -457,14 +457,14 @@ Options:
       --usage-stats <USAGE_STATS>
           Control if anonymous usage stats are collected. Options are "on", "off" and "no-stack" Defaul is on
 
-          [env: USAGE_STATS=]
-          [default: on]
-
           Possible values:
           - on:       Default option, usage statistics are collected anonymously
           - off:      Disables all collection of usage statistics
           - no-stack: Doesn't send the error stack trace or error type, but allows sending a crash event
           
+          [env: USAGE_STATS=]
+          [default: on]
+
 ```
 ## PAYLOAD_LIMIT
 ```shell