QwenLong-L1-32B-AWQ/README.md

license, tasks, base_model

license	tasks	base_model
Apache License 2.0	text-generation	iic/QwenLong-L1-32B

Intro

The AWQ version is quantized using 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

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 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
• MoE Model Quantization Script: View Here

With these scripts, you can easily complete the quantization process for the model.