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---
license: mit
tags:
- deepseek
- int4
- vllm
- llmcompressor
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
library_name: transformers
---
# DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16
## Model Overview
- **Model Architecture:** Qwen2ForCausalLM
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** INT4
- **Release Date:** 2/7/2025
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).
### Model Optimizations
This model was obtained by quantizing the weights of [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) 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-1.5B-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-1.5B"
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-1.5B-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-1.5B-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-1.5B</th>
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16</th>
<th>Recovery</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="4"><b>Reasoning</b></td>
<td>AIME 2024 (pass@1)</td>
<td>30.05</td>
<td>24.63</td>
<td>81.96%</td>
</tr>
<tr>
<td>MATH-500 (pass@1)</td>
<td>84.66</td>
<td>85.52</td>
<td>97.47%</td>
</tr>
<tr>
<td>GPQA Diamond (pass@1)</td>
<td>35.37</td>
<td>33.16</td>
<td>93.75%</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>50.03</b></td>
<td><b>46.77</b></td>
<td><b>93.48%</b></td>
</tr>
<tr>
<td rowspan="7"><b>OpenLLM V1</b></td>
<td>ARC-Challenge (Acc-Norm, 25-shot)</td>
<td>37.20</td>
<td>35.84</td>
<td>96.3%</td>
</tr>
<tr>
<td>GSM8K (Strict-Match, 5-shot)</td>
<td>69.98</td>
<td>68.01</td>
<td>97.2%</td>
</tr>
<tr>
<td>HellaSwag (Acc-Norm, 10-shot)</td>
<td>43.86</td>
<td>42.38</td>
<td>96.6%</td>
</tr>
<tr>
<td>MMLU (Acc, 5-shot)</td>
<td>37.38</td>
<td>36.98</td>
<td>98.9%</td>
</tr>
<tr>
<td>TruthfulQA (MC2, 0-shot)</td>
<td>45.21</td>
<td>46.68</td>
<td>103.3%</td>
</tr>
<tr>
<td>Winogrande (Acc, 5-shot)</td>
<td>54.30</td>
<td>55.49</td>
<td>102.2%</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>47.99</b></td>
<td><b>47.56</b></td>
<td><b>99.1%</b></td>
</tr>
<tr>
<td rowspan="7"><b>OpenLLM V2</b></td>
<td>IFEval (Inst Level Strict Acc, 0-shot)</td>
<td>34.63</td>
<td>34.21</td>
<td>98.8%</td>
</tr>
<tr>
<td>BBH (Acc-Norm, 3-shot)</td>
<td>2.40</td>
<td>2.82</td>
<td>---</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>0.93</td>
<td>1.19</td>
<td>---</td>
</tr>
<tr>
<td>MUSR (Acc-Norm, 0-shot)</td>
<td>1.26</td>
<td>1.37</td>
<td>---</td>
</tr>
<tr>
<td>MMLU-Pro (Acc, 5-shot)</td>
<td>1.32</td>
<td>1.31</td>
<td>---</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>6.80</b></td>
<td><b>6.82</b></td>
<td><b>---</b></td>
</tr>
<tr>
<td rowspan="4"><b>Coding</b></td>
<td>HumanEval (pass@1)</td>
<td>37.90</td>
<td>35.70</td>
<td><b>94.2%</b></td>
</tr>
<tr>
<td>HumanEval (pass@10)</td>
<td>61.30</td>
<td>61.40</td>
<td>100.2%</td>
</tr>
<tr>
<td>HumanEval+ (pass@10)</td>
<td>33.00</td>
<td>31.90</td>
<td>96.7%</td>
</tr>
<tr>
<td>HumanEval+ (pass@10)</td>
<td>55.90</td>
<td>55.50</td>
<td>99.3%</td>
</tr>
</tbody>
</table>
## Inference Performance
This model achieves up to 1.5x speedup in single-stream 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-1.5B-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 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>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">A6000x1</th>
<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B</th>
<td>---</td>
<td>0.8</td>
<td>5667</td>
<td>1.6</td>
<td>2776</td>
<td>0.8</td>
<td>5515</td>
<td>0.8</td>
<td>5466</td>
<td>6.4</td>
<td>705</td>
<td>6.5</td>
<td>697</td>
<td>3.5</td>
<td>1295</td>
<td>18.3</td>
<td>246</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w8a8</th>
<td>1.14</td>
<td>0.7</td>
<td>6635</td>
<td>1.3</td>
<td>3340</td>
<td>0.7</td>
<td>6396</td>
<td>0.7</td>
<td>6343</td>
<td>5.3</td>
<td>845</td>
<td>5.4</td>
<td>832</td>
<td>2.9</td>
<td>1547</td>
<td>21.3</td>
<td>211</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16</th>
<td>1.38</td>
<td>0.5</td>
<td>8293</td>
<td>1.1</td>
<td>4184</td>
<td>0.6</td>
<td>7976</td>
<td>0.6</td>
<td>7504</td>
<td>4.3</td>
<td>1051</td>
<td>4.4</td>
<td>1033</td>
<td>2.5</td>
<td>1819</td>
<td>21.1</td>
<td>213</td>
</tr>
<tr>
<th rowspan="3" valign="top">A100x1</th>
<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B</th>
<td>---</td>
<td>0.6</td>
<td>3359</td>
<td>1.2</td>
<td>1654</td>
<td>0.6</td>
<td>3286</td>
<td>0.6</td>
<td>3241</td>
<td>4.7</td>
<td>424</td>
<td>4.9</td>
<td>411</td>
<td>2.6</td>
<td>778</td>
<td>21.1</td>
<td>95</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w8a8</th>
<td>1.05</td>
<td>0.6</td>
<td>3531</td>
<td>1.1</td>
<td>1807</td>
<td>0.6</td>
<td>3427</td>
<td>0.6</td>
<td>3480</td>
<td>4.5</td>
<td>448</td>
<td>4.5</td>
<td>447</td>
<td>2.4</td>
<td>842</td>
<td>23.5</td>
<td>86</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16</th>
<td>1.03</td>
<td>0.6</td>
<td>3469</td>
<td>1.1</td>
<td>1751</td>
<td>0.6</td>
<td>3403</td>
<td>0.6</td>
<td>3407</td>
<td>4.5</td>
<td>447</td>
<td>4.6</td>
<td>435</td>
<td>2.5</td>
<td>815</td>
<td>23.3</td>
<td>86</td>
</tr>
<tr>
<th rowspan="3" valign="top">H100x1</th>
<th>deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B</th>
<td>---</td>
<td>0.4</td>
<td>2604</td>
<td>0.8</td>
<td>1299</td>
<td>0.4</td>
<td>2543</td>
<td>0.4</td>
<td>2551</td>
<td>3.3</td>
<td>330</td>
<td>3.4</td>
<td>326</td>
<td>1.8</td>
<td>612</td>
<td>14.0</td>
<td>78</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-FP8-dynamic</th>
<td>1.04</td>
<td>0.4</td>
<td>2694</td>
<td>0.8</td>
<td>1364</td>
<td>0.4</td>
<td>2670</td>
<td>0.4</td>
<td>2639</td>
<td>3.2</td>
<td>347</td>
<td>3.2</td>
<td>341</td>
<td>1.6</td>
<td>673</td>
<td>14.1</td>
<td>78</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16</th>
<td>0.84</td>
<td>0.5</td>
<td>2111</td>
<td>1.0</td>
<td>1065</td>
<td>0.5</td>
<td>2068</td>
<td>0.5</td>
<td>2119</td>
<td>4.1</td>
<td>270</td>
<td>4.1</td>
<td>265</td>
<td>2.1</td>
<td>530</td>
<td>15.1</td>
<td>73</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).

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{
"_name_or_path": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151643,
"hidden_act": "silu",
"hidden_size": 1536,
"initializer_range": 0.02,
"intermediate_size": 8960,
"max_position_embeddings": 131072,
"max_window_layers": 21,
"model_type": "qwen2",
"num_attention_heads": 12,
"num_hidden_layers": 28,
"num_key_value_heads": 2,
"quantization_config": {
"config_groups": {
"group_0": {
"input_activations": null,
"output_activations": null,
"targets": [
"Linear"
],
"weights": {
"actorder": "group",
"block_structure": null,
"dynamic": false,
"group_size": 128,
"num_bits": 4,
"observer": "mse",
"observer_kwargs": {},
"strategy": "group",
"symmetric": true,
"type": "int"
}
}
},
"format": "pack-quantized",
"global_compression_ratio": 2.0710890309058,
"ignore": [
"lm_head"
],
"kv_cache_scheme": null,
"quant_method": "compressed-tensors",
"quantization_status": "compressed",
"sparsity_config": {}
},
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 10000,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.48.0",
"use_cache": true,
"use_mrope": false,
"use_sliding_window": false,
"vocab_size": 151936
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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{
"_from_model_config": true,
"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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version https://git-lfs.github.com/spec/v1
oid sha256:89ea42cf4f9f651398ed53167f162f1b4cbe1cfacd1ef04baed49b2223731341
size 1611497856

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quant_stage:
quant_modifiers:
GPTQModifier:
dampening_frac: 0.1
ignore: [lm_head]
config_groups:
group_0:
weights: {num_bits: 4, type: int, symmetric: true, strategy: group, group_size: 128,
actorder: group, observer: mse}
targets: [Linear]

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special_tokens_map.json Normal file
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{
"bos_token": {
"content": "<｜begin▁of▁sentence｜>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
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