Updated doc

docs/source/basic_tutorials/launcher.md

@@ -55,10 +55,17 @@ Options:
 #QUANTIZE
 ```shell
       --quantize <QUANTIZE>
-          Whether you want the model to be quantized. This will use `bitsandbytes` for quantization on the fly, or `gptq`. 4bit quantization is available through `bitsandbytes` by providing the `bitsandbytes-fp4` or `bitsandbytes-nf4` options
+          Whether you want the model to be quantized
 
           [env: QUANTIZE=]
-          [possible values: bitsandbytes, bitsandbytes-nf4, bitsandbytes-fp4, gptq]
+
+          Possible values:
+          - awq:              4 bit quantization. Requires a specific GTPQ quantized model: https://hf.co/models?search=awq. Should replace GPTQ models whereever possible because of the better latency
+          - eetq:             8 bit quantization, doesn't require specific model. Should be a drop-in replacement to bitsandbytes with much better performance. Kernels are from https://github.com/NetEase-FuXi/EETQ.git
+          - gptq:             4 bit quantization. Requires a specific GTPQ quantized model: https://hf.co/models?search=gptq. text-generation-inference will use exllama (faster) kernels whereever possible, and use triton kernel (wider support) when it's not. AWQ has faster kernels
+          - bitsandbytes:     Bitsandbytes 8bit. Can be applied on any model, will cut the memory requirement in half, but it is known that the model will be much slower to run than the native f16
+          - bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16
+          - bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
 
 ```
 #DTYPE
@@ -104,6 +111,15 @@ Options:
           [env: MAX_STOP_SEQUENCES=]
           [default: 4]
 
+```
+#MAX_TOP_N_TOKENS
+```shell
+      --max-top-n-tokens <MAX_TOP_N_TOKENS>
+          This is the maximum allowed value for clients to set `top_n_tokens`. `top_n_tokens is used to return information about the the `n` most likely tokens at each generation step, instead of just the sampled token. This information can be used for downstream tasks like for classification or ranking
+          
+          [env: MAX_TOP_N_TOKENS=]
+          [default: 5]
+
 ```
 #MAX_INPUT_LENGTH
 ```shell