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Model: swift/QwenLong-L1-32B-AWQ
Source: Original Platform

README.md

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+---
+license: Apache License 2.0
+tasks:
+- text-generation
+base_model:
+  - iic/QwenLong-L1-32B
+---
+
+## Intro
+
+The AWQ version is quantized using [ms-swift](https://github.com/modelscope/ms-swift). Note that the AWQ version for QwenLong-L1-32B models are verified to be working on Transformers/vLLM. We have not have the chance to tested them on other engines.
+
+## Inference
+
+```python
+from modelscope import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "swift/QwenLong-L1-32B-AWQ"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# prepare the model input
+template = """Please read the following text and answer the question below.
+
+<text>
+$DOC$
+</text>
+
+$Q$
+
+Format your response as follows: "Therefore, the answer is (insert answer here)"."""
+context = "<YOUR_CONTEXT_HERE>" 
+question = "<YOUR_QUESTION_HERE>"
+prompt = template.replace('$DOC$', context.strip()).replace('$Q$', question.strip())
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=10000,
+    temperature=0.7,
+    top_p=0.95
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+
+# parsing thinking content
+try:
+    # rindex finding 151649 (</think>)
+    index = len(output_ids) - output_ids[::-1].index(151649)
+except ValueError:
+    index = 0
+
+thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
+content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
+
+print("thinking content:", thinking_content)
+print("content:", content)
+```
+
+## Quantization
+
+The model has undergone AWQ int4 quantization using the [ms-swift](https://github.com/modelscope/ms-swift) framework.
+
+If you have fine-tuned the model and wish to quantize the fine-tuned version, you can refer to the following quantization scripts:
+
+- Dense Model Quantization Script: [View Here](https://github.com/modelscope/ms-swift/blob/main/examples/export/quantize/awq.sh)
+- MoE Model Quantization Script: [View Here](https://github.com/modelscope/ms-swift/blob/main/examples/export/quantize/moe/awq.sh)
+
+With these scripts, you can easily complete the quantization process for the model.