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Qwen2-Audio-7B-Instruct_eagle3

Name: Qwen2-Audio-7B-Instruct_eagle3
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Dedicated to building a more intuitive, comprehensive, and efficient LLMs compression toolkit.

📖 Documentation | 🤗 Hugging Face | 🤖 ModelScope | 💬 WeChat

📣Latest News

[26/01/13] We have released v0.3. We support the training and deployment of Eagle3 for all-scale LLMs/VLMs/Audio models, as detailed in the guidance documentation. And We released Sherry, the hardware-efficient 1.25 bit quantization algorithm [Paper Comming soon] | [Code]🔥🔥🔥
[25/11/05] We have released v0.2. Quantization support for new models, such as GLM-4.6, Qwen3-VL and Qwen3-Omni, open-sources the Eagle3 speculative decoding training framework, and updates the Diffusion model quantization tools.
[25/09/30] We have released SpecExit, the reasoning early-exit algorithm: [Paper] | [Docs] | [vLLM Code]
[25/09/26] We have released TEQUILA, the ternary quantization algorithm [Paper] | [Code]
[25/09/24] We now support the PTQ quantization of NVFP4 for the Qwen3 series models. We also opensource Qwen3-32B-NVFP4 and Qwen3-235B-A22B-NVFP4 weights.

Previous News

[25/09/01] We now support FP8 quantization of the Hunyuan-MT-7B translation model. And enabled Torch inference and Benchmark evaluation for Eagle3. And implemented support for quantization and Cache for FLUX. And support quantization for the Seed-OSS.
[25/08/06] We now support quantization for Hunyuan 0.5B/1.8B/4B/7B and multimodal model Qwen2.5VL 3B/7B/32B/72B, including FP8/INT4 algorithms, and quantization for DeepSeek-R1/V3 and Kimi-K2, including FP8-Static and W4A8-FP8 algorithms. We also opensource Hunyuan 1.8B/4B/7B series Eagle3 model weight.
[25/07/04] We now support quantization for Hunyuan/Qwen2.5/Qwen3/DeepSeek-R1-Distill-Qwen and other models, including INT8/FP8/INT4 algorithms. We also opensource Qwen3 series Eagle3 model weight.

🌟Key Features

Highly Integrated: This toolkit integrates mainstream compression algorithms into a unified framework, offering developers one-click access with exceptional ease of use.
Continuous Innovation: Beyond integrating widely-used industry algorithms, we are continuously researching better compression algorithms, which will be gradually open-sourced in the future.
Performance-Driven: We continuously optimize end-to-end performance in model compression workflows and algorithm deployment, such as enabling quantization of models like Qwen3-235B and DeepSeek-R1 on a single GPU.

💼Technical Overview

Scenario	Model	Compression Strategy
Quantization	Speculative Decoding	Other Techniques
Large Language Models (LLMs)	Hunyuan-Dense Hunyuan-MoE Qwen3 DeepSeek-V3/R1 GLM-4.6 Qwen2.5	FP8-Static/Dynamic INT8-Dynamic INT4-GPTQ/AWQ/GPTAQ NVFP4 LeptoQuant Tequila	Eagle3 SpecExit	Sparse Attention Under Development
Vision Language Models (VLMs)	Hunyuan-VL HunyuanOCR Qwen3-VL Qwen2.5-VL	FP8-Static/Dynamic INT8-Dynamic INT4-GPTQ/AWQ/GPTAQ	Eagle3	Token Pruning Under Development
Diffusion Models	Hunyuan-Image Hunyuan-Video Hunyuan-3D Qwen-Image FLUX Wan SDXL	FP8-Dynamic FP8-Weight-Only	-	Cache DeepCache TeaCache TaylorCache Sparse Attention Under Development
Speech Models (TTS/ASR)	Qwen3-Omni Qwen2-Audio Fun-CosyVoice3	FP8-Static/Dynamic INT8-Dynamic	Eagle3	Token Pruning Under Development

🛎️How to Use

1. Install AngelSlim

We recommend using pip to install the latest stable version of AngelSlim:

pip install angelslim

Alternatively, you can clone the repository and install from source in editable mode:

cd AngelSlim && python setup.py install

For more detailed installation instructions, please refer to the Installation Documentation.

2. Quick Start

2.1 Speculative Decoding

After installing AngelSlim, you can quickly start Eagle3 training with the following scripts:

shell

# Start the vLLM server
bash scripts/speculative/run_vllm_server.sh
# Generate training data
bash scripts/speculative/generate_data_for_target_model.sh
# Perform online training for the Eagle3 model
bash scripts/speculative/train_eagle3_online.sh

Training and Deployment Guide for Multimodal Model Eagle3—Supporting LLM, VLM, and Audio (ASR & TTS) Models: LLM | VLM | Audio(ASR) | Audio(TTS).

2.2 LLM/VLM Model Quantization

After installing AngelSlim, you can launch static FP8 quantization for the Qwen3-1.7B model with the following one-command script:

python3 tools/run.py -c configs/qwen3/fp8_static/qwen3-1_7b_fp8_static.yaml

This example produces quantized model weights by performing PTQ calibration on a model loaded from HuggingFace.

Code-based Start

To perform dynamic FP8 quantization on Qwen3-1.7B:

python

from angelslim.engine import Engine

slim_engine = Engine()
# Prepare model
slim_engine.prepare_model(model_name="Qwen", model_path="Qwen/Qwen3-1.7B",)
# Initialize compressor
slim_engine.prepare_compressor("PTQ", default_method="fp8_dynamic")
# Compress model
slim_engine.run()
# Save compressed model
slim_engine.save("./output")

For more details, please refer to the Quick Start Documentation.

2.3 Diffusion Model Quantization

Use the scripts/diffusion/run_diffusion.py for quantization and inference:

shell

# Online quantization and inference
python scripts/diffusion/run_diffusion.py \
  --model-name-or-path black-forest-labs/FLUX.1-schnell \
  --quant-type fp8-per-tensor \
  --prompt "A cat holding a sign that says hello world" \
  --height 1024 --width 1024 --steps 4 --guidance 0.0 --seed 0

For more quantization inference methods, please refer to the Diffusion Model Quantization Documentation.

3. Deployment and Testing

3.1 Offline Inference

To test offline inference with a quantized model loaded via transformers, run the following command:

python scripts/deploy/offline.py $MODEL_PATH "Hello, my name is"

Where MODEL_PATH is the path to the quantized model output.

3.2 API Service Deployment

After specifying the quantized model path MODEL_PATH, you can deploy an OpenAI-compatible API service using the following LLMs inference frameworks:

vLLM

Use the following script to launch a vLLM server, recommended version vllm>=0.8.5.post1. For MOE INT8 quantized models, vllm>=0.9.0 is required.

bash scripts/deploy/run_vllm.sh --model-path $MODEL_PATH --port 8080 -d 0,1,2,3 -t 4 -p 1 -g 0.8 --max-model-len 4096

Where -d is the visible devices, -t is tensor parallel size, -p is pipeline parallel size, and -g is the GPU memory utilization.
SGLang

Use the following script to launch a SGLang server, recommended version sglang>=0.4.6.post1.

bash scripts/deploy/run_sglang.sh --model-path $MODEL_PATH --port 8080 -d 0,1,2,3 -t 4 -g 0.8

3.3 Service Invocation

Invoke requests via OpenAI's API format:

bash scripts/deploy/openai.sh -m $MODEL_PATH -p "Hello, my name is" --port 8080 --max-tokens 4096 --temperature 0.7 --top-p 0.8 --top-k 20 --repetition-penalty 1.05 --system-prompt "You are a helpful assistant."

where -p is the input prompt.

3.4 Performance Evaluation

Evaluate the performance of quantized model using lm-evaluation-harness, recommended versionlm-eval>=0.4.8

Run script details

bash scripts/deploy/lm_eval.sh -d 0,1 -t 2 -g 0.8 -r $RESULT_PATH -b "auto" --tasks ceval-valid,mmlu,gsm8k,humaneval -n 0 $MODEL_PATH

where RESULT_PATH is the directory for saving test results, -b is batch size, --tasks specifies the evaluation tasks, and -n is the number of few-shot examples.

For more detaileds, please refer to the Deployment Documentation.

📈 Benchmark

1. Speculative Decoding

We evaluated the Eagle3 model trained by AngelSlim on tasks including code generation, mathematical reasoning, instruction following, text generation, and multimodal understanding using vLLM. The inference acceleration and context length performance of our trained model under the settings of num_speculative_tokens = 2 or 4 are presented as follows, with an accept length of 1.8–3.5 and a maximum speedup of 1.4–1.9×.

1.1 Qwen3 Series Models

Benchmark results for Qwen3 series models using Eagle3 speculative decoding on vLLM (v0.11.2) across MT-bench, HumanEval, GSM8K and Alpaca, using a single NVIDIA H20 GPU (tp=1, ep=1, num_speculative_tokens=2, batch_size=1, output_len=1024).

Model	Method	GSM8K	Alpaca	HumanEval	MT-bench	Mean
		throughput (tokens/s)	accept length	throughput (tokens/s)	accept length	throughput (tokens/s)	accept length	throughput (tokens/s)	accept length	throughput (tokens/s)	accept length
Qwen3-1.7B	Vanilla	376.42	1	378.86	1	378.38	1	390.53	1	381.05	1
Eagle3	616.9	2.13	653.29	2.19	680.1	2.2	621.44	2.17	642.93	2.17
Qwen3-4B	Vanilla	229.05	1	235.29	1	234.66	1	234.04	1	233.26	1
Eagle3	389.35	2.07	395.97	2.1	377.84	2.08	384.6	2.07	386.94	2.08
Qwen3-8B	Vanilla	149.63	1	149.93	1	153.85	1	153.81	1	151.81	1
Eagle3	257.32	2	266.69	2.02	244.89	1.97	258.2	1.97	257.52	1.99
Qwen3-14B	Vanilla	92.97	1	92.66	1	92.94	1	94.46	1	93.26	1
Eagle3	153.72	1.87	140.46	1.78	144.68	1.76	142.45	1.74	145.33	1.79
Qwen3-32B	Vanilla	43.49	1	43.38	1	43.19	1	43.3	1	43.32	1
Eagle3	80.43	2.01	72.49	1.9	71.57	1.86	74.1	1.86	74.1	1.91
Qwen3-30B-A3B	Vanilla	311.84	1	320.43	1	325.77	1	325.42	1	320.87	1
Eagle3	453.97	2.1	432.45	2.04	428.81	2.02	437.06	2.01	438.07	2.04

1.2 VLM Models

1.2.1 Qwen3-VL Series Models

Benchmark results for Qwen3-VL series models using Eagle3 speculative decoding on vLLM (v0.12.0) across language and multimodal tasks, using a single NVIDIA H20 GPU (tp=1, ep=1, num_speculative_tokens=4, batch_size=1, output_len=1024).

Model	Method	GSM8K	Alpaca	HumanEval	MT-bench	MATH-500	MMMU	MMStar	Mean
		throughput (tokens/s)	accept length	throughput (tokens/s)	accept length	throughput (tokens/s)	accept length	throughput (tokens/s)	accept length	throughput (tokens/s)	accept length	throughput (tokens/s)	accept length	throughput (tokens/s)	accept length	throughput (tokens/s)	accept length
Qwen3-VL-2B-Instruct	Vanilla	348.55	1	350.9	1	346.07	1	346.31	1	82.96	1	83.27	1	81.63	1	234.24	1
Eagle3	511.52	2.11	560.55	2.26	826.01	3.39	555.22	2.29	163.09	2.57	154.18	2.55	139.73	2.31	415.76	2.5
Qwen3-VL-4B-Instruct	Vanilla	212.87	1	213.24	1	211.69	1	212.1	1	67.96	1	65.88	1	67.75	1	150.21	1
Eagle3	415.29	2.57	372.89	2.26	459.37	2.82	382.33	2.34	141.87	2.72	104.44	2.05	107.07	2.1	283.32	2.41
Qwen3-VL-30B-A3B-Instruct	Vanilla	179.94	1	184.6	1	168.68	1	180.57	1	31.08	1	31.51	1	30.93	1	115.33	1
Eagle3	281.93	2.82	241.42	2.13	223.05	2.57	240.47	2.19	75.31	2.79	48.47	1.78	52.57	1.94	166.17	2.32

1.2.2 HunyuanOCR Model

Benchmark results for HunyuanOCR using Eagle3 speculative decoding on vLLM (v0.13.0) across OmniDocBench dataset, using a single NVIDIA H20 GPU (tp=1, ep=1, num_speculative_tokens=4, batch_size=1, output_len=1024).

Model	Method	OmniDocBench
		throughput (tokens/s)	accept length
Hunyuan-OCR	Vanilla	70.12	1
Eagle3	108.1	2.08

1.3 Audio Models

1.3.1 Qwen2-Audio Model

Benchmark results for Qwen2-Audio using Eagle3 speculative decoding on vLLM (v0.12.0) across LibriSpeech dataset, using a single NVIDIA H20 GPU (tp=1, ep=1, num_speculative_tokens=4, batch_size=1, output_len=1024).

Model	Method	LibriSpeech
		throughput (tokens/s)	accept length
Qwen2-Audio	Vanilla	78.76	1
Eagle3	146.66	3.51

1.3.2 Fun-CosyVoice3 Model

Benchmark results for Fun-CosyVoice3 using Eagle3 speculative decoding across LibriTTS dataset, using a single NVIDIA H20 GPU (tp=1, ep=1, num_speculative_tokens=4, batch_size=1, output_len=1024).

Model	Method	LibriTTS
		throughput (tokens/s)	accept length
Fun-CosyVoice3	Vanilla	-	1
Eagle3	-	1.96

Adapted for Transformers backend inference, only displays accept length. vLLM speedup ~1.6×, estimated from baseline LLM speedup.

2. Quantization

The performance test results for selected models are shown below. For the complete benchmark, refer to the Benchmark documentation

2.1 Hunyuan Series Models

Benchmark results for the Hunyuan-Instruct model with FP8, INT4-AWQ and INT4-GPTQ quantization algorithms on datasets includingOlympiadBench, AIME 2024 and DROP:

Model	Quantization	OlympiadBench	AIME 2024	DROP	GPQA-Diamond
Hunyuan-A13B-Instruct	BF16	82.7	87.30	91.1	71.2
FP8-Static	83.0	86.7	91.1	-
Int4-GPTQ	82.7	86.7	91.1	-
Int4-AWQ	82.6	85.6	91.0	-
Hunyuan-7B-Instruct	BF16	76.5	81.1	85.9	60.1
FP8-Static	76.6	80.9	86.0	60.1
Int4-GPTQ	76.2	81.0	85.7	60.0
Int4-AWQ	76.4	80.9	85.9	60.1
Hunyuan-4B-Instruct	BF16	73.1	78.3	78.2	61.1
FP8-Static	73.1	76.6	78.3	60.2
Int4-GPTQ	72.9	-	78.1	58.1
Int4-AWQ	72.8	-	78.2	-
Hunyuan-1.8B-Instruct	BF16	63.4	56.7	76.7	47.2
FP8-Static	62.5	55.2	75.1	47.7
Int4-GPTQ	60.9	-	73.0	44.4
Int4-AWQ	61.7	-	71.7	43.6
Hunyuan-0.5B-Instruct	BF16	29.6	17.2	52.8	23.3
FP8-Static	29.6	17.2	51.6	22.5
Int4-GPTQ	26.8	-	50.9	23.3
Int4-AWQ	26.3	-	48.9	23.3

2.2 Qwen3 Series Models

Benchmark results for Qwen3 series models with FP8-Static, FP8-Dynamic, INT4-GPTQ, and INT4-AWQ quantization algorithms on datasets including CEVAL, MMLU, GSM8K, and HUMANEVAL:

Model	Quantization	CEVAL	MMLU	GSM8K	HUMANEVAL
Qwen3-0.6B	BF16	45.84	47.21	42.99	19.51
FP8-Static	45.99	46.87	38.06	18.90
FP8-Dynamic	45.99	46.93	38.29	20.73
INT8-Dynamic	45.17	46.95	41.17	21.34
Qwen3-8B	BF16	79.27	74.78	87.79	63.41
FP8-Static	78.23	74.79	86.96	62.20
FP8-Dynamic	78.45	74.75	87.64	62.80
INT8-Dynamic	78.01	74.84	86.96	67.07
INT4-GPTQ	77.19	73.26	86.43	62.20
INT4-AWQ	76.15	73.59	86.96	63.41
Qwen3-14B	BF16	83.06	78.90	88.40	55.49
FP8-Static	82.62	78.57	89.46	57.32
FP8-Dynamic	82.24	78.92	88.32	52.44
INT8-Dynamic	81.87	78.13	86.28	56.10
INT4-GPTQ	81.05	78.02	87.34	57.93
INT4-AWQ	82.02	77.68	84.23	61.59
Qwen3-32B	BF16	86.55	82.00	74.53	37.80
FP8-Static	86.92	81.78	70.20	39.63
FP8-Dynamic	86.55	81.89	70.43	38.41
INT4-GPTQ	86.18	81.01	-	43.29
INT4-AWQ	86.18	81.54	-	36.59
Qwen3-30B-A3B	BF16	83.66	79.36	89.99	31.71
FP8-Static	83.95	79.47	89.01	31.10
FP8-Dynamic	84.10	79.40	89.16	32.93
INT8-Dynamic	83.36	79.48	89.16	34.15
Qwen3-235B-A22B	BF16	89.60	86.28	85.29	27.44
FP8-Static	89.67	86.19	86.96	27.44
FP8-Dynamic	89.67	86.18	85.22	28.05
INT8-Dynamic	88.93	86.20	86.20	23.78

2.3 DeepSeek Series Models

Benchmark results for DeepSeek-R1-0528 series models with FP8-Block-Wise and W4A8-FP8 quantization algorithms on datasets including GPQA Diamond、AIME 2024、SimpleQA and LiveCodeBench：

Model	Quantization	GPQA Diamond	AIME 2024	SimpleQA	LiveCodeBench
DeepSeek-R1-0528	FP8-Block-Wise	78.28	88.67	27.8	77.1
W4A8-FP8	77.37	88.67	26.83	78.86

Note

The above results are based on the average of 5 test runs deployed with TRT-LLM

The hyperparameters used during evaluation are as follows:

json
1{ 2 "top_k": 20, 3 "top_p": 0.6, 4 "temperature": 0.7, 5 "output_seq_len": 32768, 6 "max_input_seq_len": 16384 7}

2.4 Qwen-VL Series Models

Qwen3-VL Benchmark

Benchmark results for Qwen3VL series models with BF16、FP8-Static and FP8-Dynamic quantization algorithms on datasets including MMMU_VAL、DocVQA_VAL and ChartQA_TEST：

Model	Quantization	MMMU_VAL	DocVQA_VAL	ChartQA_TEST
Qwen3-VL-32B-Instruct	BF16	60.11	96.08	94.64
FP8-Static	61.22	96.00	94.64
FP8-Dynamic	60.78	96.19	94.72
Qwen3-VL-30B-A3B-Instruct	BF16	50.44	95.28	95.36
FP8-Dynamic	50.67	95.25	95.20

Qwen2.5VL Benchmark

Benchmark results for Qwen2.5VL series models with BF16、FP8-Static、FP8-Dynamic、INT4-GPTQ、INT4-AWQ quantization algorithms on datasets including MMMU_VAL、DocVQA_VAL and ChartQA_TEST：

Model	Quantization	MMMU_VAL	MMLDocVQA_VALU	ChartQA_TEST
Qwen2.5VL-3B	BF16	47.11	78.57	80.32
FP8-Static	47.33	79.34	79.68
FP8-Dynamic	45.99	46.93	38.29
INT4-GPTQ	46.56	77.20	78.96
INT4-AWQ	45.78	-	79.60
Qwen2.5VL-7B	BF16	45.44	89.71	84.64
FP8-Static	47.00	89.83	85.92
FP8-Dynamic	47.22	89.80	88.64
INT4-GPTQ	46.67	90.45	-
INT4-AWQ	45.67	89.28	-
Qwen2.5VL-32B	BF16	57.00	90.03	-
FP8-Static	57.00	89.88	-
FP8-Dynamic	56.44	89.88	-
INT4-GPTQ	55.22	89.80	-
INT4-AWQ	55.22	90.30	-
Qwen2.5VL-72B	BF16	58.78	94.39	85.60
FP8-Static	57.89	94.41	85.84
FP8-Dynamic	58.67	94.38	85.60
INT4-GPTQ	57.56	94.46	86.48
INT4-AWQ	58.78	94.19	87.28

2.5 Qwen-Omni Series Models

Qwen3-Omni Text to Text Benchmark

Benchmark results for Qwen3-Omni series models in BF16, FP8-Static, and FP8-Dynamic on aime25, gpqa_diamond, and mmlu_redux are as follows:

Model	Quantization	aime25	gpqa_diamond	mmlu_redux
Qwen3-Omni-30B-A3B-Instruct	BF16	73.32	56.77	88.09
FP8-Static	71.33	56.57	87.91
FP8-Dynamic	73.33	55.15	88.07