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Model: RedHatAI/QwQ-32B-Preview-quantized.w8a8
Source: Original Platform

.gitattributes

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README.md

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+---
+tags:
+- w8a8
+- int8
+- vllm
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE
+language:
+  - en
+base_model: Qwen/Qwen2.5-32B-Instruct
+library_name: transformers
+---
+
+# QwQ-32B-Preview-quantized.w8a8
+
+## Model Overview
+- **Model Architecture:** QwQ-32B-Preview
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT8
+  - **Activation quantization:** INT8
+- **Release Date:** 3/1/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview).
+It achieves an average score of 76.49 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 77.20.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations of [QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) to INT8 data type, ready for inference with vLLM >= 0.5.2.
+This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. 
+
+## Deployment
+
+### Use with vLLM
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+max_model_len, tp_size = 4096, 1
+model_name = "neuralmagic-ent/QwQ-32B-Preview-quantized.w8a8"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
+sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
+
+messages_list = [
+    [{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
+]
+
+prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
+
+outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
+
+generated_text = [output.outputs[0].text for output in outputs]
+print(generated_text)
+```
+
+vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+## Creation
+
+This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below with the following arguments:
+
+
+```bash
+python quantize.py --model_path Qwen/QwQ-32B-Preview --quant_path "output_dir/QwQ-32B-Preview-quantized.w8a8" --calib_size 1024 --dampening_frac 0.1 --observer mse 
+```
+
+
+```python
+from datasets import load_dataset
+from transformers import AutoTokenizer
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot, apply
+import argparse
+from compressed_tensors.quantization import QuantizationScheme, QuantizationArgs, QuantizationType, QuantizationStrategy
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model_path', type=str)
+parser.add_argument('--quant_path', type=str)
+parser.add_argument('--calib_size', type=int, default=256)
+parser.add_argument('--dampening_frac', type=float, default=0.1) 
+parser.add_argument('--observer', type=str, default="minmax")
+args = parser.parse_args()
+
+model = SparseAutoModelForCausalLM.from_pretrained(
+    args.model_path,
+    device_map="auto",
+    torch_dtype="auto",
+    use_cache=False,
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(args.model_path)
+
+NUM_CALIBRATION_SAMPLES = args.calib_size
+DATASET_ID = "garage-bAInd/Open-Platypus"
+DATASET_SPLIT = "train"
+ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
+ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
+
+def preprocess(example):
+    concat_txt = example["instruction"] + "\n" + example["output"]
+    return {"text": concat_txt}
+
+ds = ds.map(preprocess)
+
+def tokenize(sample):
+    return tokenizer(
+        sample["text"],
+        padding=False,
+        truncation=False,
+        add_special_tokens=True,
+    )
+
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+
+recipe = [
+    GPTQModifier(
+        targets=["Linear"],
+        ignore=["lm_head"],
+        scheme="W8A8",
+        dampening_frac=args.dampening_frac,
+        observer=args.observer,
+    )
+]
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    num_calibration_samples=args.calib_size,
+    max_seq_length=8192,
+)
+
+# Save to disk compressed.
+SAVE_DIR = args.quant_path
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)
+```
+
+## Evaluation
+
+The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) and [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/), using the following commands:
+
+OpenLLM Leaderboard V1:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/QwQ-32B-Preview-quantized.w8a8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
+  --tasks openllm \
+  --write_out \
+  --batch_size auto \
+  --output_path output_dir \
+  --show_config
+```
+
+OpenLLM Leaderboard V2:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/QwQ-32B-Preview-quantized.w8a8",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
+  --apply_chat_template \
+  --fewshot_as_multiturn \
+  --tasks leaderboard \
+  --write_out \
+  --batch_size auto \
+  --output_path output_dir \
+  --show_config
+
+```
+
+### Accuracy
+
+#### OpenLLM Leaderboard V1 evaluation scores
+
+| Metric                                   | Qwen/QwQ-32B-Preview             | neuralmagic-ent/QwQ-32B-Preview-quantized.w8a8 |
+|-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
+| ARC-Challenge (Acc-Norm, 25-shot)       | 70.73                            | 70.73                                       |
+| GSM8K (Strict-Match, 5-shot)            | 83.09                            | 79.91                                       |
+| HellaSwag (Acc-Norm, 10-shot)           | 85.77                            | 85.75                                       |
+| MMLU (Acc, 5-shot)                      | 82.67                            | 82.24                                       |
+| TruthfulQA (MC2, 0-shot)                | 60.88                            | 59.18                                       |
+| Winogrande (Acc, 5-shot)                | 80.03                            | 81.14                                       |
+| **Average Score**                       | **77.20**                        | **76.49**                                   |
+| **Recovery**                            | **100.00**                       | **99.08**                                   |
+
+#### OpenLLM Leaderboard V2 evaluation scores
+
+| Metric                                                   | Qwen/QwQ-32B-Preview             | neuralmagic-ent/QwQ-32B-Preview-quantized.w8a8 |
+|---------------------------------------------------------|:---------------------------------:|:-------------------------------------------:|
+| IFEval (Inst-and-Prompt Level Strict Acc, 0-shot)       | 42.34                            | 43.49                                       |
+| BBH (Acc-Norm, 3-shot)                                  | 53.03                            | 52.95                                       |
+| Math-Hard (Exact-Match, 4-shot)                         | 21.15                            | 22.36                                       |
+| GPQA (Acc-Norm, 0-shot)                                 | 2.97                             | 3.5                                        |
+| MUSR (Acc-Norm, 0-shot)                                 | 9.57                             | 10.87                                       |
+| MMLU-Pro (Acc, 5-shot)                                  | 52.00                            | 51.4                                       |
+| **Average Score**                                       | **30.18**                        | **30.76**                                   |
+| **Recovery**                                            | **100.00**                       | **101.92**                                   |
+

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configuration.json

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generation_config.json

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+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.47.1"
+}

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quant_w8a8.sh

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+#!/bin/bash
+
+    # for DAMPENING_FRAC 0.01 0.1;
+    # do
+    #     for OBSERVER minmax mse;
+    #     do
+    #         for ACTORDER False group;
+    #         do
+
+# export DAMPENING_FRAC=0.01
+# export OBSERVER="minmax"
+
+export CUDA_VISIBLE_DEVICES=${1}
+export MDL=${2}
+export OBSERVER=${3} # minmax mse
+export DAMPENING_FRAC=${4} # 0.01 0.1
+
+for CALIB_SIZE in 128 512 1024;
+do
+    python w8a8.py --model_path ${MDL} --quant_path "output_dir_w8a8/${MDL}/calib${CALIB_SIZE}_eosFalse_damp${DAMPENING_FRAC}_obs${OBSERVER}" --calib_size ${CALIB_SIZE} --dampening_frac ${DAMPENING_FRAC} --observer ${OBSERVER}
+done
+

recipe.yaml

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+DEFAULT_stage:
+  DEFAULT_modifiers:
+    GPTQModifier:
+      targets: [Linear]
+      dampening_frac: 0.1
+      ignore: [lm_head]
+      scheme: W8A8

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+    "<|im_end|>",
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+    "lstrip": false,
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+    "content": "<|endoftext|>",
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+  }
+}

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