--- license: mit tags: - deepseek - int4 - vllm - llmcompressor base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B library_name: transformers --- # DeepSeek-R1-Distill-Qwen-14B-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-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B). ### Model Optimizations This model was obtained by quantizing the weights of [DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) 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-14B-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-14B" 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=0.01, ) # 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-14B-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-14B-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
Category Metric deepseek-ai/DeepSeek-R1-Distill-Qwen-14B neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16 Recovery
OpenLLM V1 ARC-Challenge (Acc-Norm, 25-shot) 58.79 58.28 99.1%
GSM8K (Strict-Match, 5-shot) 87.04 87.34 100.4%
HellaSwag (Acc-Norm, 10-shot) 81.51 80.42 98.7%
MMLU (Acc, 5-shot) 74.46 73.32 98.5%
TruthfulQA (MC2, 0-shot) 54.77 55.29 101.0%
Winogrande (Acc, 5-shot) 69.38 70.48 101.6%
Average Score 70.99 70.85 99.8%
OpenLLM V2 IFEval (Inst Level Strict Acc, 0-shot) 43.05 34.90 81.1%
BBH (Acc-Norm, 3-shot) 47.16 45.36 96.2%
Math-Hard (Exact-Match, 4-shot) 0.00 0.00 ---
GPQA (Acc-Norm, 0-shot) 35.07 34.90 99.5%
MUSR (Acc-Norm, 0-shot) 45.14 44.20 97.9%
MMLU-Pro (Acc, 5-shot) 34.86 35.09 100.7%
Average Score 34.21 32.41 94.7%
Coding HumanEval (pass@1) 78.90 79.00 100.1%
HumanEval (pass@10) 89.80 89.70 99.9%
HumanEval+ (pass@10) 72.60 72.80 100.3%
HumanEval+ (pass@10) 84.90 84.00 98.8%
## Inference Performance This model achieves up to 2.8x speedup in single-stream deployment and up to 1.4x 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).
Benchmarking Command ``` guidellm --model neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16 --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=,generated_tokens=" --max seconds 360 --backend aiohttp_server ```
### Single-stream performance (measured with vLLM version 0.7.2)
Instruction Following
256 / 128		 Multi-turn Chat
512 / 256		 Docstring Generation
768 / 128		 RAG
1024 / 128		 Code Completion
256 / 1024		 Code Fixing
1024 / 1024		 Large Summarization
4096 / 512		 Large RAG
10240 / 1536
Hardware Model Average cost reduction Latency (s) QPD Latency (s) QPD Latency (s) QPD Latency (s) QPD Latency (s) QPD Latency (s) QPD Latency (s) QPD Latency (s) QPD
A6000x1 deepseek-ai/DeepSeek-R1-Distill-Qwen-14B --- 5.4 837 10.7 419 5.5 813 5.6 805 42.2 107 42.8 105 22.9 197 71.7 63
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8 1.59 3.3 1345 6.7 673 3.4 1315 3.5 1296 26.5 170 26.8 168 14.5 310 48.3 93
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16 2.51 2.0 2275 4.0 1127 2.2 2072 2.3 1945 15.3 294 15.9 283 9.9 456 36.6 123
A100x1 deepseek-ai/DeepSeek-R1-Distill-Qwen-14B --- 2.6 765 5.2 383 2.7 746 2.7 732 20.8 97 21.2 95 11.3 179 36.7 55
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8 1.34 1.9 1072 3.8 533 1.9 1045 1.9 1032 14.8 136 15.2 132 8.1 248 39.6 51
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16 1.93 1.2 1627 2.5 810 1.3 1530 1.4 1474 9.7 208 10.2 197 5.8 348 37.6 53
H100x1 deepseek-ai/DeepSeek-R1-Distill-Qwen-14B --- 1.6 672 3.3 334 1.7 662 1.7 652 12.8 85 13.0 84 7.0 155 25.2 43
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-FP8-dynamic 1.33 1.2 925 2.3 467 1.2 908 1.2 896 9.3 118 9.5 115 5.2 210 23.9 46
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16 1.37 1.2 944 2.3 474 1.2 931 1.2 907 9.1 121 9.2 119 5.1 214 22.5 49
**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)
Instruction Following
256 / 128		 Multi-turn Chat
512 / 256		 Docstring Generation
768 / 128		 RAG
1024 / 128		 Code Completion
256 / 1024		 Code Fixing
1024 / 1024		 Large Summarization
4096 / 512		 Large RAG
10240 / 1536
Hardware Model Average cost reduction Maximum throughput (QPS) QPD Maximum throughput (QPS) QPD Maximum throughput (QPS) QPD Maximum throughput (QPS) QPD Maximum throughput (QPS) QPD Maximum throughput (QPS) QPD Maximum throughput (QPS) QPD Maximum throughput (QPS) QPD
A6000x1 deepseek-ai/DeepSeek-R1-Distill-Qwen-14B --- 13.7 30785 5.5 12327 6.5 14517 5.1 11439 2.0 4434 1.3 2982 0.6 1462 0.2 371
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8 1.44 21.4 48181 8.2 18421 9.8 22051 7.8 17462 2.8 6281 1.7 3758 1.0 2335 0.2 419
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16 0.98 12.7 28540 5.7 12796 5.4 12218 3.7 8401 2.5 5583 1.3 2987 0.7 1489 0.2 368
A100x1 deepseek-ai/DeepSeek-R1-Distill-Qwen-14B --- 15.6 31306 7.1 14192 7.7 15435 6.0 11971 2.4 4878 1.6 3298 0.9 1862 0.2 355
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8 1.31 20.8 41907 9.3 18724 10.5 21043 8.4 16886 3.0 5975 1.9 3917 1.2 2481 0.2 464
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16 0.94 14.0 28146 6.5 13042 6.5 12987 5.1 10194 2.6 5269 1.5 2925 0.9 1849 0.2 382
H100x1 deepseek-ai/DeepSeek-R1-Distill-Qwen-14B --- 31.4 34404 14.1 15482 16.6 18149 13.3 14572 4.7 5099 2.6 2849 1.9 2060 0.3 347
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-FP8-dynamic 1.31 40.9 44729 18.5 20260 22.1 24165 18.1 19779 5.7 6246 3.4 3681 2.5 2746 0.4 474
neuralmagic/DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16 1.12 33.3 36387 15.0 16453 17.6 19241 14.2 15576 4.6 5034 3.0 3292 2.2 2412 0.4 481
**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).