Update README.md

README.md

@@ -36,7 +36,7 @@ This model was obtained by quantizing the weights of [Meta-Llama-3.1-8B-Instruct
 This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
 
 Only the weights of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the INT4 and floating point representations of the quantized weights.
-The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library. GPTQ used a 1% damping factor and 512 sequences of 8,192 random tokens.
+The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) library. GPTQ used a 1% damping factor and 512 sequences of 8,192 random tokens.
 
 
 ## Deployment
@@ -75,7 +75,8 @@ vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://do
 
 ## Creation
 
-This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below.
+This model was created by applying the [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) library as presented in the code snipet below.
+Although AutoGPTQ was used for this particular model, Neural Magic is transitioning to using [llm-compressor](https://github.com/vllm-project/llm-compressor) which supports several quantization schemes and models not supported by AutoGPTQ.
 
 ```python
 from transformers import AutoTokenizer