Model Architecture: Qwen2ForCausalLM
- Input: Text
- Output: Text
Model Optimizations:
- Weight quantization: FP8
- Activation quantization: FP8
Release Date: 2/5/2025
Version: 1.0
Model Developers: Neural Magic

Quantized version of DeepSeek-R1-Distill-Qwen-1.5B.

Model Optimizations

This model was obtained by quantizing the weights and activations of DeepSeek-R1-Distill-Qwen-1.5B to FP8 data type. 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. Weights are quantized using a symmetric per-channel scheme, whereas quantizations are quantized using a symmetric per-token scheme. LLM Compressor is used for quantization.

Use with vLLM

This model can be deployed efficiently using the vLLM 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-dynamic"

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 for more details.

Creation

This model was created with llm-compressor by running the code snippet below.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
import os

# Load model
model_stub = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub,
    torch_dtype="auto",
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

# Configure the quantization algorithm and scheme
recipe = QuantizationModifier(
    targets="Linear",
    scheme="FP8_DYNAMIC",
    ignore=["lm_head"],
)

# Apply quantization
oneshot(
    model=model,
    recipe=recipe,
)

# Save to disk in compressed-tensors format
save_path = model_name + "-FP8-dynamic
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 and V2, using the following commands:

OpenLLM Leaderboard V1:

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-FP8-dynamic",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-FP8-dynamic",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-1.5B	neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-FP8-dynamic	Recovery
Reasoning	AIME 2024 (pass@1)	30.05	29.83	99.27%
MATH-500 (pass@1)	84.66	84.74	100.09%
GPQA Diamond (pass@1)	35.37	35.93	101.58%
Average Score	50.03	50.17	100.28%
OpenLLM V1	ARC-Challenge (Acc-Norm, 25-shot)	37.20	37.71	101.4%
GSM8K (Strict-Match, 5-shot)	69.98	68.99	98.6%
HellaSwag (Acc-Norm, 10-shot)	43.86	43.61	99.4%
MMLU (Acc, 5-shot)	37.38	37.22	99.6%
TruthfulQA (MC2, 0-shot)	45.21	44.77	99.0%
Winogrande (Acc, 5-shot)	54.30	54.62	100.6%
Average Score	47.99	47.82	99.7%
OpenLLM V2	IFEval (Inst Level Strict Acc, 0-shot)	34.63	34.91	100.8%
BBH (Acc-Norm, 3-shot)	3.06	2.40	---
Math-Hard (Exact-Match, 4-shot)	0.00	0.00	---
GPQA (Acc-Norm, 0-shot)	1.01	0.93	---
MUSR (Acc-Norm, 0-shot)	0.78	1.26	---
MMLU-Pro (Acc, 5-shot)	1.32	1.25	---
Average Score	6.80	6.79	---
Coding	HumanEval (pass@1)	37.90	36.40	96.0%
HumanEval (pass@10)	61.30	61.30	100.0%
HumanEval+ (pass@10)	33.00	32.60	98.8%
HumanEval+ (pass@10)	55.90	56.30	100.7%

Inference Performance

This model achieves up to 1.1x speedup in single-stream deployment, depending on hardware and use-case scenario. The following performance benchmarks were conducted with vLLM version 0.7.2, and GuideLLM.

Benchmarking Command

guidellm --model neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-FP8-dynamic --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_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-1.5B	---	0.8	5667	1.6	2776	0.8	5515	0.8	5466	6.4	705	6.5	697	3.5	1295	18.3	246
neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w8a8	1.14	0.7	6635	1.3	3340	0.7	6396	0.7	6343	5.3	845	5.4	832	2.9	1547	21.3	211
neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16	1.38	0.5	8293	1.1	4184	0.6	7976	0.6	7504	4.3	1051	4.4	1033	2.5	1819	21.1	213
A100x1	deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B	---	0.6	3359	1.2	1654	0.6	3286	0.6	3241	4.7	424	4.9	411	2.6	778	21.1	95
neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w8a8	1.05	0.6	3531	1.1	1807	0.6	3427	0.6	3480	4.5	448	4.5	447	2.4	842	23.5	86
neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16	1.03	0.6	3469	1.1	1751	0.6	3403	0.6	3407	4.5	447	4.6	435	2.5	815	23.3	86
H100x1	deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B	---	0.4	2604	0.8	1299	0.4	2543	0.4	2551	3.3	330	3.4	326	1.8	612	14.0	78
neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-FP8-dynamic	1.04	0.4	2694	0.8	1364	0.4	2670	0.4	2639	3.2	347	3.2	341	1.6	673	14.1	78
neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16	0.84	0.5	2111	1.0	1065	0.5	2068	0.5	2119	4.1	270	4.1	265	2.1	530	15.1	73

**Use case profiles: prompt tokens / generation tokens

**QPD: Queries per dollar, based on on-demand cost at Lambda Labs (observed on 2/18/2025).

RedHatAI/DeepSeek-R1-Distill-Qwen-1.5B-FP8-dynamic

DeepSeek-R1-Distill-Qwen-1.5B-FP8-dynamic

Model Overview