Add pipeline_tag and arxiv ID, fix usage snippet (#1)

- Add pipeline_tag and arxiv ID, fix usage snippet (1f08de3fbf006eb7e29f41749622ad21fc13f4d6)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,5 +1,6 @@
 ---
-language: 
+base_model: Qwen/Qwen3-32B
+language:
 - en
 license: apache-2.0
 tags:
@@ -11,8 +12,9 @@ tags:
 - qwen
 - llm
 - compression
-base_model: Qwen/Qwen3-32B
 base_model_relation: quantized
+pipeline_tag: text-generation
+arxiv: 2509.22944
 ---
 
 <p align="center">
@@ -48,7 +50,7 @@ To support the project please put a star ⭐ in the official [SINQ](https://gith
 
 ---
 
-# 🚀 Usage</span>
+# 🚀 Usage
 
 ## Prerequisite
 Before running the quantization script, make sure the **SINQ** library is installed.
@@ -58,6 +60,7 @@ Installation instructions and setup details are available in the [SINQ official
 You can load and use the model with our wrapper based on the 🤗 Transformers library:
 
 ```python
+import torch
 from transformers import AutoTokenizer
 from sinq.patch_model import AutoSINQHFModel
 
@@ -83,13 +86,14 @@ print(tokenizer.decode(out_ids[0], skip_special_tokens=True))
 The quantized model was obtained using the **SINQ** quantization library, following the steps below:
 
 ```python
+import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from sinq.patch_model import AutoSINQHFModel
 from sinq.sinqlinear import BaseQuantizeConfig
 
 # Load base model
 base_model_name = "Qwen/Qwen3-32B"
-model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype="float16")
+model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float16)
 tokenizer = AutoTokenizer.from_pretrained(base_model_name)
 
 # Apply 4-bit SINQ quantization