84 lines
2.5 KiB
Markdown
84 lines
2.5 KiB
Markdown
---
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license: Apache License 2.0
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tasks:
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- text-generation
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base_model:
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- iic/QwenLong-L1-32B
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---
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## Intro
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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.
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## Inference
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```python
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from modelscope import AutoModelForCausalLM, AutoTokenizer
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model_name = "swift/QwenLong-L1-32B-AWQ"
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# load the tokenizer and the model
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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# prepare the model input
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template = """Please read the following text and answer the question below.
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<text>
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$DOC$
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</text>
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$Q$
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Format your response as follows: "Therefore, the answer is (insert answer here)"."""
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context = "<YOUR_CONTEXT_HERE>"
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question = "<YOUR_QUESTION_HERE>"
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prompt = template.replace('$DOC$', context.strip()).replace('$Q$', question.strip())
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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# conduct text completion
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=10000,
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temperature=0.7,
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top_p=0.95
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)
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
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# parsing thinking content
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try:
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# rindex finding 151649 (</think>)
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index = len(output_ids) - output_ids[::-1].index(151649)
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except ValueError:
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index = 0
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thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
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content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
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print("thinking content:", thinking_content)
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print("content:", content)
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```
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## Quantization
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The model has undergone AWQ int4 quantization using the [ms-swift](https://github.com/modelscope/ms-swift) framework.
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If you have fine-tuned the model and wish to quantize the fine-tuned version, you can refer to the following quantization scripts:
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- Dense Model Quantization Script: [View Here](https://github.com/modelscope/ms-swift/blob/main/examples/export/quantize/awq.sh)
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- MoE Model Quantization Script: [View Here](https://github.com/modelscope/ms-swift/blob/main/examples/export/quantize/moe/awq.sh)
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With these scripts, you can easily complete the quantization process for the model.
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