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Model: RedHatAI/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16
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README.md

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+---
+license: mit
+tags:
+- deepseek
+- int4
+- vllm
+- llmcompressor
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
+library_name: transformers
+---
+
+# DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16
+
+## Model Overview
+- **Model Architecture:** Qwen2ForCausalLM
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT4
+- **Release Date:** 2/4/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B).
+
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) to INT4 data type.
+This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
+
+
+Only the weights of the linear operators within transformers blocks are quantized.
+Weights are quantized using a symmetric per-group scheme, with group size 128.
+The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
+
+
+## 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
+
+number_gpus = 1
+model_name = "neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+sampling_params = SamplingParams(temperature=0.6, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
+llm = LLM(model=model_name, tensor_parallel_size=number_gpus, trust_remote_code=True)
+
+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. 
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
+from llmcompressor.transformers import oneshot
+
+# Load model
+model_stub = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
+model_name = model_stub.split("/")[-1]
+
+num_samples = 2048
+max_seq_len = 8192
+
+tokenizer = AutoTokenizer.from_pretrained(model_stub)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_stub,
+    device_map="auto",
+    torch_dtype="auto",
+)
+
+def preprocess_fn(example):
+  return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
+
+ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
+ds = ds.map(preprocess_fn)
+
+# Configure the quantization algorithm and scheme
+recipe = QuantizationModifier(
+    targets="Linear",
+    scheme="W4A16",
+    ignore=["lm_head"],
+    dampening_frac=0.1,
+)
+
+# Apply quantization
+oneshot(
+    model=model,
+    dataset=ds, 
+    recipe=recipe,
+    max_seq_length=max_seq_len,
+    num_calibration_samples=num_samples,
+)
+
+# Save to disk in compressed-tensors format
+save_path = model_name + "-quantized.w4a16
+model.save_pretrained(save_path)
+tokenizer.save_pretrained(save_path)
+print(f"Model and tokenizer saved to: {save_path}")
+```
+
+## 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/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=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/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=True \
+  --apply_chat_template \
+  --fewshot_as_multiturn \
+  --tasks leaderboard \
+  --write_out \
+  --batch_size auto \
+  --output_path output_dir \
+  --show_config
+```
+
+### Accuracy
+
+<table>
+  <thead>
+    <tr>
+      <th>Category</th>
+      <th>Metric</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th>
+      <th>Recovery</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+<td rowspan="4"><b>Reasoning</b></td>
+<td>AIME 2024 (pass@1)</td>
+<td>69.75</td>
+<td>68.83</td>
+<td>98.68%</td>
+</tr>
+<tr>
+<td>MATH-500 (pass@1)</td>
+<td>95.09</td>
+<td>95.01</td>
+<td>99.92%</td>
+</tr>
+<tr>
+<td>GPQA Diamond (pass@1)</td>
+<td>64.05</td>
+<td>63.79</td>
+<td>99.59%</td>
+</tr>
+<tr>
+<td><b>Average Score</b></td>
+<td><b>76.3</b></td>
+<td><b>75.88</b></td>
+<td><b>99.45%</b></td>
+</tr>
+    <tr>
+      <td rowspan="7"><b>OpenLLM V1</b></td>
+      <td>ARC-Challenge (Acc-Norm, 25-shot)</td>
+      <td>64.59</td>
+      <td>63.65</td>
+      <td>98.6%</td>
+    </tr>
+    <tr>
+      <td>GSM8K (Strict-Match, 5-shot)</td>
+      <td>82.71</td>
+      <td>84.38</td>
+      <td>102.0%</td>
+    </tr>
+    <tr>
+      <td>HellaSwag (Acc-Norm, 10-shot)</td>
+      <td>83.80</td>
+      <td>83.32</td>
+      <td>99.4%</td>
+    </tr>
+    <tr>
+      <td>MMLU (Acc, 5-shot)</td>
+      <td>81.12</td>
+      <td>80.91</td>
+      <td>99.7%</td>
+    </tr>
+    <tr>
+      <td>TruthfulQA (MC2, 0-shot)</td>
+      <td>58.41</td>
+      <td>58.54</td>
+      <td>100.2%</td>
+    </tr>
+    <tr>
+      <td>Winogrande (Acc, 5-shot)</td>
+      <td>76.40</td>
+      <td>75.14</td>
+      <td>98.4%</td>
+    </tr>
+    <tr>
+      <td><b>Average Score</b></td>
+      <td><b>74.51</b></td>
+      <td><b>74.32</b></td>
+      <td><b>99.8%</b></td>
+    </tr>
+    <tr>
+      <td rowspan="7"><b>OpenLLM V2</b></td>
+      <td>IFEval (Inst Level Strict Acc, 0-shot)</td>
+      <td>42.87</td>
+      <td>72.48</td>
+      <td>99.1%</td>
+    </tr>
+    <tr>
+      <td>BBH (Acc-Norm, 3-shot)</td>
+      <td>57.96</td>
+      <td>57.54</td>
+      <td>99.3%</td>
+    </tr>
+    <tr>
+      <td>Math-Hard (Exact-Match, 4-shot)</td>
+      <td>0.00</td>
+      <td>0.00</td>
+      <td>---</td>
+    </tr>
+    <tr>
+      <td>GPQA (Acc-Norm, 0-shot)</td>
+      <td>26.95</td>
+      <td>26.41</td>
+      <td>98.0%</td>
+    </tr>
+    <tr>
+      <td>MUSR (Acc-Norm, 0-shot)</td>
+      <td>43.95</td>
+      <td>44.34</td>
+      <td>100.9%</td>
+    </tr>
+    <tr>
+      <td>MMLU-Pro (Acc, 5-shot)</td>
+      <td>49.82</td>
+      <td>48.43</td>
+      <td>97.2%</td>
+    </tr>
+    <tr>
+      <td><b>Average Score</b></td>
+      <td><b>36.92</b></td>
+      <td><b></b></td>
+      <td><b>%</b></td>
+    </tr>
+    <tr>
+      <td rowspan="4"><b>Coding</b></td>
+      <td>HumanEval (pass@1)</td>
+      <td>86.00</td>
+      <td>86.00</td>
+      <td><b>100.0%</b></td>
+    </tr>
+    <tr>
+      <td>HumanEval (pass@10)</td>
+      <td>92.50</td>
+      <td>92.60</td>
+      <td>100.1%</td>
+    </tr>
+    <tr>
+      <td>HumanEval+ (pass@10)</td>
+      <td>82.00</td>
+      <td>80.60</td>
+      <td>98.3%</td>
+    </tr>
+    <tr>
+      <td>HumanEval+ (pass@10)</td>
+      <td>88.70</td>
+      <td>89.70</td>
+      <td>101.1%</td>
+    </tr>
+  </tbody>
+</table>
+
+## Inference Performance
+
+
+This model achieves up to 3.4x speedup in single-stream deployment and up to 2.0x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario.
+The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm).
+
+<details>
+<summary>Benchmarking Command</summary>
+
+```
+guidellm --model neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16 --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server
+```
+</details>
+
+### Single-stream performance (measured with vLLM version 0.7.2)
+<table>
+  <thead>
+    <tr>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th style="text-align: center;" colspan="2" >Instruction Following<br>256 / 128</th>
+      <th style="text-align: center;" colspan="2" >Multi-turn Chat<br>512 / 256</th>
+      <th style="text-align: center;" colspan="2" >Docstring Generation<br>768 / 128</th>
+      <th style="text-align: center;" colspan="2" >RAG<br>1024 / 128</th>
+      <th style="text-align: center;" colspan="2" >Code Completion<br>256 / 1024</th>
+      <th style="text-align: center;" colspan="2" >Code Fixing<br>1024 / 1024</th>
+      <th style="text-align: center;" colspan="2" >Large Summarization<br>4096 / 512</th>
+      <th style="text-align: center;" colspan="2" >Large RAG<br>10240 / 1536</th>
+    </tr>
+    <tr>
+      <th>GPU class</th>
+      <th>Number of GPUs</th>
+      <th>Model</th>
+      <th>Average cost reduction</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+    </tr>
+  </thead>
+  <tbody style="text-align: center" >
+    <tr>
+      <th rowspan="3" valign="top">A6000</th>
+      <td>2</td>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th>
+      <td>---</td>
+      <td>6.3</td>
+      <td>359</td>
+      <td>12.8</td>
+      <td>176</td>
+      <td>6.5</td>
+      <td>347</td>
+      <td>6.6</td>
+      <td>342</td>
+      <td>49.9</td>
+      <td>45</td>
+      <td>50.8</td>
+      <td>44</td>
+      <td>26.6</td>
+      <td>85</td>
+      <td>83.4</td>
+      <td>27</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w8a8</th>
+      <td>1.81</td>
+      <td>6.9</td>
+      <td>648</td>
+      <td>13.8</td>
+      <td>325</td>
+      <td>7.2</td>
+      <td>629</td>
+      <td>7.2</td>
+      <td>622</td>
+      <td>54.8</td>
+      <td>82</td>
+      <td>55.6</td>
+      <td>81</td>
+      <td>30.0</td>
+      <td>150</td>
+      <td>94.8</td>
+      <td>47</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th>
+      <td>3.07</td>
+      <td>3.9</td>
+      <td>1168</td>
+      <td>7.8</td>
+      <td>580</td>
+      <td>4.3</td>
+      <td>1041</td>
+      <td>4.6</td>
+      <td>975</td>
+      <td>29.7</td>
+      <td>151</td>
+      <td>30.9</td>
+      <td>146</td>
+      <td>19.3</td>
+      <td>233</td>
+      <td>61.4</td>
+      <td>73</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">A100</th>
+      <td>1</td>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th>
+      <td>---</td>
+      <td>5.6</td>
+      <td>361</td>
+      <td>11.1</td>
+      <td>180</td>
+      <td>5.7</td>
+      <td>350</td>
+      <td>5.8</td>
+      <td>347</td>
+      <td>44.0</td>
+      <td>46</td>
+      <td>44.7</td>
+      <td>45</td>
+      <td>23.6</td>
+      <td>85</td>
+      <td>73.7</td>
+      <td>27</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w8a8</th>
+      <td>1.50</td>
+      <td>3.7</td>
+      <td>547</td>
+      <td>7.3</td>
+      <td>275</td>
+      <td>3.8</td>
+      <td>536</td>
+      <td>3.8</td>
+      <td>528</td>
+      <td>29.0</td>
+      <td>69</td>
+      <td>29.5</td>
+      <td>68</td>
+      <td>15.7</td>
+      <td>128</td>
+      <td>53.1</td>
+      <td>38</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th>
+      <td>2.30</td>
+      <td>2.2</td>
+      <td>894</td>
+      <td>4.5</td>
+      <td>449</td>
+      <td>2.4</td>
+      <td>831</td>
+      <td>2.5</td>
+      <td>798</td>
+      <td>17.4</td>
+      <td>116</td>
+      <td>18.0</td>
+      <td>112</td>
+      <td>10.5</td>
+      <td>191</td>
+      <td>49.5</td>
+      <td>41</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">H100</th>
+      <td>1</td>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th>
+      <td>---</td>
+      <td>3.3</td>
+      <td>327</td>
+      <td>6.7</td>
+      <td>163</td>
+      <td>3.4</td>
+      <td>320</td>
+      <td>3.4</td>
+      <td>317</td>
+      <td>26.6</td>
+      <td>41</td>
+      <td>26.9</td>
+      <td>41</td>
+      <td>14.3</td>
+      <td>77</td>
+      <td>47.8</td>
+      <td>23</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-FP8-dynamic</th>
+      <td>1.52</td>
+      <td>2.2</td>
+      <td>503</td>
+      <td>4.3</td>
+      <td>252</td>
+      <td>2.2</td>
+      <td>490</td>
+      <td>2.3</td>
+      <td>485</td>
+      <td>17.3</td>
+      <td>63</td>
+      <td>17.5</td>
+      <td>63</td>
+      <td>9.5</td>
+      <td>116</td>
+      <td>33.4</td>
+      <td>33</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th>
+      <td>1.61</td>
+      <td>2.1</td>
+      <td>532</td>
+      <td>4.1</td>
+      <td>268</td>
+      <td>2.1</td>
+      <td>516</td>
+      <td>2.1</td>
+      <td>513</td>
+      <td>16.1</td>
+      <td>68</td>
+      <td>16.5</td>
+      <td>66</td>
+      <td>9.1</td>
+      <td>120</td>
+      <td>31.9</td>
+      <td>34</td>
+    </tr>
+  </tbody>
+</table>
+
+**Use case profiles: prompt tokens / generation tokens
+
+**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).
+
+
+### Multi-stream asynchronous performance (measured with vLLM version 0.7.2)
+<table>
+  <thead>
+    <tr>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th style="text-align: center;" colspan="2" >Instruction Following<br>256 / 128</th>
+      <th style="text-align: center;" colspan="2" >Multi-turn Chat<br>512 / 256</th>
+      <th style="text-align: center;" colspan="2" >Docstring Generation<br>768 / 128</th>
+      <th style="text-align: center;" colspan="2" >RAG<br>1024 / 128</th>
+      <th style="text-align: center;" colspan="2" >Code Completion<br>256 / 1024</th>
+      <th style="text-align: center;" colspan="2" >Code Fixing<br>1024 / 1024</th>
+      <th style="text-align: center;" colspan="2" >Large Summarization<br>4096 / 512</th>
+      <th style="text-align: center;" colspan="2" >Large RAG<br>10240 / 1536</th>
+    </tr>
+    <tr>
+      <th>Hardware</th>
+      <th>Model</th>
+      <th>Average cost reduction</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+    </tr>
+  </thead>
+  <tbody style="text-align: center" >
+    <tr>
+      <th rowspan="3" valign="top">A6000x2</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th>
+      <td>---</td>
+      <td>6.2</td>
+      <td>13940</td>
+      <td>1.9</td>
+      <td>4348</td>
+      <td>2.7</td>
+      <td>6153</td>
+      <td>2.1</td>
+      <td>4778</td>
+      <td>0.6</td>
+      <td>1382</td>
+      <td>0.4</td>
+      <td>930</td>
+      <td>0.3</td>
+      <td>685</td>
+      <td>0.1</td>
+      <td>124</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w8a8</th>
+      <td>1.80</td>
+      <td>8.7</td>
+      <td>19492</td>
+      <td>4.2</td>
+      <td>9474</td>
+      <td>4.1</td>
+      <td>9290</td>
+      <td>3.0</td>
+      <td>6802</td>
+      <td>1.2</td>
+      <td>2734</td>
+      <td>0.9</td>
+      <td>1962</td>
+      <td>0.5</td>
+      <td>1177</td>
+      <td>0.1</td>
+      <td>254</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th>
+      <td>1.30</td>
+      <td>5.9</td>
+      <td>13366</td>
+      <td>2.5</td>
+      <td>5733</td>
+      <td>2.4</td>
+      <td>5409</td>
+      <td>1.6</td>
+      <td>3525</td>
+      <td>1.2</td>
+      <td>2757</td>
+      <td>0.7</td>
+      <td>1663</td>
+      <td>0.3</td>
+      <td>676</td>
+      <td>0.1</td>
+      <td>214</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">A100x2</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th>
+      <td>---</td>
+      <td>12.9</td>
+      <td>13016</td>
+      <td>5.8</td>
+      <td>5848</td>
+      <td>6.3</td>
+      <td>6348</td>
+      <td>5.1</td>
+      <td>5146</td>
+      <td>2.0</td>
+      <td>1988</td>
+      <td>1.5</td>
+      <td>1463</td>
+      <td>0.9</td>
+      <td>869</td>
+      <td>0.2</td>
+      <td>192</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w8a8</th>
+      <td>1.52</td>
+      <td>21.4</td>
+      <td>21479</td>
+      <td>8.9</td>
+      <td>8948</td>
+      <td>10.6</td>
+      <td>10611</td>
+      <td>8.2</td>
+      <td>8197</td>
+      <td>3.0</td>
+      <td>3018</td>
+      <td>2.0</td>
+      <td>2054</td>
+      <td>1.2</td>
+      <td>1241</td>
+      <td>0.3</td>
+      <td>264</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th>
+      <td>1.09</td>
+      <td>13.5</td>
+      <td>13568</td>
+      <td>6.5</td>
+      <td>6509</td>
+      <td>6.0</td>
+      <td>6075</td>
+      <td>4.7</td>
+      <td>4754</td>
+      <td>2.8</td>
+      <td>2790</td>
+      <td>1.6</td>
+      <td>1651</td>
+      <td>0.9</td>
+      <td>862</td>
+      <td>0.2</td>
+      <td>225</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">H100x2</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-32B</th>
+      <td>---</td>
+      <td>25.5</td>
+      <td>14392</td>
+      <td>12.5</td>
+      <td>7035</td>
+      <td>14.0</td>
+      <td>7877</td>
+      <td>11.3</td>
+      <td>6364</td>
+      <td>3.6</td>
+      <td>2041</td>
+      <td>2.7</td>
+      <td>1549</td>
+      <td>1.9</td>
+      <td>1057</td>
+      <td>0.4</td>
+      <td>200</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-FP8-dynamic</th>
+      <td>1.46</td>
+      <td>46.7</td>
+      <td>25538</td>
+      <td>20.3</td>
+      <td>11082</td>
+      <td>23.3</td>
+      <td>12728</td>
+      <td>18.4</td>
+      <td>10049</td>
+      <td>5.3</td>
+      <td>2881</td>
+      <td>3.7</td>
+      <td>2097</td>
+      <td>2.6</td>
+      <td>1445</td>
+      <td>0.5</td>
+      <td>256</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16</th>
+      <td>1.23</td>
+      <td>36.9</td>
+      <td>20172</td>
+      <td>17.4</td>
+      <td>9500</td>
+      <td>18.0</td>
+      <td>9822</td>
+      <td>14.2</td>
+      <td>7755</td>
+      <td>5.3</td>
+      <td>2900</td>
+      <td>3.3</td>
+      <td>1867</td>
+      <td>2.3</td>
+      <td>1265</td>
+      <td>0.4</td>
+      <td>241</td>
+    </tr>
+  </tbody>
+</table>
+
+**Use case profiles: prompt tokens / generation tokens
+
+**QPS: Queries per second.
+
+**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).

config.json

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+{
+  "_name_or_path": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 27648,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 64,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 64,
+  "num_key_value_heads": 8,
+  "quantization_config": {
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+          "observer": "mse",
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+          "type": "int"
+        }
+      }
+    },
+    "format": "pack-quantized",
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+    "ignore": [
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+    "kv_cache_scheme": null,
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {}
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

configuration.json

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generation_config.json

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+  "bos_token_id": 151646,
+  "do_sample": true,
+  "eos_token_id": 151643,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "4.48.0"
+}

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recipe.yaml

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+    GPTQModifier:
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+      ignore: [lm_head]
+      config_groups:
+        group_0:
+          weights: {num_bits: 4, type: int, symmetric: true, strategy: group, group_size: 128,
+            actorder: weight, observer: mse}
+          targets: [Linear]

special_tokens_map.json

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