--- license: mit tags: - deepseek - int8 - vllm - llmcompressor base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B library_name: transformers --- # DeepSeek-R1-Distill-Llama-8B-quantized.w8a8 ## Model Overview - **Model Architecture:** LlamaForCausalLM - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT8 - **Activation quantization:** INT8 - **Release Date:** 2/1/2025 - **Version:** 1.0 - **Model Developers:** Neural Magic Quantized version of [DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B). ### Model Optimizations This model was obtained by quantizing the weights and activations of [DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) to INT8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized using a symmetric per-channel scheme, whereas quantizations are quantized using a symmetric per-token scheme. 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-Llama-8B-quantized.w8a8" 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-Llama-8B" model_name = model_stub.split("/")[-1] num_samples = 1024 max_seq_len = 8192 tokenizer = AutoTokenizer.from_pretrained(model_stub) model = AutoModelForCausalLM.from_pretrained( model_stub, device_map=device_map, 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 = [ SmoothQuantModifier(smoothing_strength=0.8), QuantizationModifier( targets="Linear", scheme="W8A8", 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.w8a8 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-Llama-8B-quantized.w8a8",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-Llama-8B-quantized.w8a8",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-Llama-8B neuralmagic/DeepSeek-R1-Distill-Llama-8B-quantized.w8a8 Recovery
OpenLLM V1 ARC-Challenge (Acc-Norm, 25-shot) 45.05 45.22 100.4%
GSM8K (Strict-Match, 5-shot) 62.77 62.09 98.9%
HellaSwag (Acc-Norm, 10-shot) 76.78 76.80 100.0%
MMLU (Acc, 5-shot) 55.65 55.53 99.8%
TruthfulQA (MC2, 0-shot) 50.55 49.89 98.7%
Winogrande (Acc, 5-shot) 68.51 67.40 98.4%
Average Score 59.88 59.49 99.3%
OpenLLM V2 IFEval (Inst Level Strict Acc, 0-shot) 38.34 39.07 101.9%
BBH (Acc-Norm, 3-shot) 38.19 39.57 103.6%
Math-Hard (Exact-Match, 4-shot) 0.00 0.00 ---
GPQA (Acc-Norm, 0-shot) 28.87 27.28 94.5%
MUSR (Acc-Norm, 0-shot) 33.31 34.50 103.6%
MMLU-Pro (Acc, 5-shot) 20.10 20.60 102.4%
Average Score 26.47 26.84 101.4%
Coding HumanEval (pass@1) 49.90 50.90 102.0%
HumanEval (pass@10) 68.90 68.70 99.7%
HumanEval+ (pass@10) 44.10 46.70 105.9%
HumanEval+ (pass@10) 62.90 64.30 102.2%
## Inference Performance This model achieves up to 1.6x 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-Llama-8B-quantized.w8a8 --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-Llama-8B --- 3.0 1511 6.0 755 3.0 1483 3.1 1462 23.6 191 24.0 188 12.7 353 41.1 110
neuralmagic/DeepSeek-R1-Distill-Llama-8B-quantized.w8a8 1.53 1.9 2356 3.8 1175 2.0 2291 2.0 2207 15.2 297 15.5 290 8.5 531 28.6 157
neuralmagic/DeepSeek-R1-Distill-Llama-8B-quantized.w4a16 2.35 1.2 3870 2.3 1918 1.3 3492 1.3 3335 9.1 492 9.5 472 5.8 771 22.7 198
A100x1 deepseek-ai/DeepSeek-R1-Distill-Llama-8B --- 1.5 1308 3.1 657 1.6 1274 1.6 1263 12.1 166 12.4 162 6.5 308 25.6 78
neuralmagic/DeepSeek-R1-Distill-Llama-8B-quantized.w8a8 1.30 1.1 1763 2.3 882 1.2 1716 1.2 1698 9.0 223 9.2 218 4.9 409 25.7 78
neuralmagic/DeepSeek-R1-Distill-Llama-8B-quantized.w4a16 1.76 0.8 2501 1.6 1236 0.9 2350 0.9 2287 6.4 316 6.6 306 3.7 544 24.7 82
H100x1 deepseek-ai/DeepSeek-R1-Distill-Llama-8B --- 1.0 1146 1.9 574 1.0 1128 1.0 1111 7.6 144 7.7 142 4.1 266 16.3 67
neuralmagic/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic 1.25 0.7 1567 1.4 758 0.7 1484 0.7 1462 5.7 191 5.8 189 3.2 347 22.5 49
neuralmagic/DeepSeek-R1-Distill-Llama-8B-quantized.w4a16 1.30 0.7 1527 1.4 768 0.7 1495 0.7 1463 5.6 194 5.7 190 3.1 350 14.7 74
**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-Llama-8B --- 12.6 56742 5.7 25687 6.5 29349 5.2 23259 1.6 7250 1.2 5181 0.8 3445 0.1 616
neuralmagic/DeepSeek-R1-Distill-Llama-8B-quantized.w8a8 1.34 17.4 78101 7.6 34351 8.8 39790 7.0 31532 2.3 10405 1.5 6960 1.0 4355 0.2 785
neuralmagic/DeepSeek-R1-Distill-Llama-8B-quantized.w4a16 0.91 10.9 48964 5.1 22989 4.8 21791 3.8 17039 2.2 9726 1.2 5434 0.6 2544 0.1 578
A100x1 deepseek-ai/DeepSeek-R1-Distill-Llama-8B --- 24.5 49296 11.3 22657 13.0 26047 10.5 21020 3.5 7029 2.5 4995 1.7 3503 0.3 659
neuralmagic/DeepSeek-R1-Distill-Llama-8B-quantized.w8a8 1.27 30.8 62042 14.1 28419 17.2 34554 13.8 27719 4.6 9299 3.1 6215 2.2 4331 0.4 807
neuralmagic/DeepSeek-R1-Distill-Llama-8B-quantized.w4a16 0.97 22.7 45708 10.5 21216 11.1 22353 8.9 17939 3.9 7758 2.6 5241 1.6 3196 0.4 718
H100x1 deepseek-ai/DeepSeek-R1-Distill-Llama-8B --- 49.0 53593 22.6 24750 28.3 30971 22.9 25035 7.2 7912 5.1 5561 3.6 3939 0.6 703
neuralmagic/DeepSeek-R1-Distill-Llama-8B-FP8-dynamic 1.14 57.1 62517 26.0 28440 34.5 37781 28.7 31360 7.2 7877 5.4 5923 4.3 4697 0.7 782
neuralmagic/DeepSeek-R1-Distill-Llama-8B-quantized.w4a16 1.01 49.8 54452 22.9 25035 28.5 31162 23.0 25200 6.8 7493 5.0 5431 3.7 4079 0.7 787
**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).