pytorch
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Qwen3-32B-float8dq

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 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- torchao
+license: apache-2.0
+language:
+- multilingual
+base_model:
+- Qwen/Qwen3-32B
+pipeline_tag: text-generation
 ---
+[Qwen3-32B](https://huggingface.co/Qwen3/Qwen3-32B) model quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) float8 dynamic activation and float8 weight quantization (per row granularity), by PyTorch team. Use it directly, or serve using [vLLM](https://docs.vllm.ai/en/latest/) with TODO VRAM reduction, TODO speedup and little to no accuracy impact on H100.
+# Inference with vLLM
+TODO
+# Inference with Transformers
+TODO
+# Quantization Recipe
+Install the required packages:
+```Shell
+pip install git+https://github.com/huggingface/transformers@main
+pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
+pip install torch
+pip install accelerate
+```
+Use the following code to get the quantized model:
+```Py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
+model_id = "Qwen/Qwen3-32B"
+from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, PerRow
+quant_config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())
+quantization_config = TorchAoConfig(quant_type=quant_config)
+quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
+)
+model_inputs = tokenizer([text], return_tensors="pt").to("cuda")
+# conduct text completion
+generated_ids = quantized_model.generate(
+    **model_inputs,
+    max_new_tokens=32768
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+# parsing thinking content
+try:
+    # rindex finding 151668 (</think>)
+    index = len(output_ids) - output_ids[::-1].index(151668)
+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)
+```
+# Model Quality
+TODO
+# Peak Memory Usage
+TODO
+# Model Performance
+TODO
+# Disclaimer
+PyTorch has not performed safety evaluations or red teamed the quantized models. Performance characteristics, outputs, and behaviors may differ from the original models. Users are solely responsible for selecting appropriate use cases, evaluating and mitigating for accuracy, safety, and fairness, ensuring security, and complying with all applicable laws and regulations.
+Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the licenses the models are released under, including any limitations of liability or disclaimers of warranties provided therein.