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--- |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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tags: |
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- fp8 |
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- quantized |
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- llm-compressor |
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- compressed-tensors |
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- red hat |
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base_model: |
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- Qwen/Qwen3-30B-A3B |
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--- |
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# Qwen3-30B-A3B-FP8-block |
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## Model Overview |
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- **Model Architecture:** Qwen3MoeForCausalLM |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Weight quantization:** FP8 |
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- **Activation quantization:** FP8 |
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- **Release Date:** |
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- **Version:** 1.0 |
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- **Model Developers:**: Red Hat |
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Quantized version of [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B). |
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### Model Optimizations |
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This model was obtained by quantizing the weights and activations of [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) to FP8 data type. |
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. |
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Only the weights and activations of the linear operators within transformers blocks of the language model are quantized. |
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## Deployment |
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### Use with vLLM |
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1. Initialize vLLM server: |
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``` |
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vllm serve RedHatAI/Qwen3-30B-A3B-FP8-BLOCK --tensor_parallel_size 4 |
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``` |
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2. Send requests to the server: |
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```python |
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from openai import OpenAI |
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# Modify OpenAI's API key and API base to use vLLM's API server. |
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openai_api_key = "EMPTY" |
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openai_api_base = "http://<your-server-host>:8000/v1" |
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client = OpenAI( |
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api_key=openai_api_key, |
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base_url=openai_api_base, |
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) |
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model = "RedHatAI/Qwen3-30B-A3B-FP8-BLOCK" |
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messages = [ |
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{"role": "user", "content": "Explain quantum mechanics clearly and concisely."}, |
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] |
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outputs = client.chat.completions.create( |
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model=model, |
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messages=messages, |
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) |
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generated_text = outputs.choices[0].message.content |
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print(generated_text) |
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``` |
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## Creation |
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This model was quantized using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as shown below. |
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<details> |
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<summary>Creation details</summary> |
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```python |
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from transformers import AutoProcessor, Qwen3MoeForCausalLM |
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from llmcompressor import oneshot |
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from llmcompressor.modeling import replace_modules_for_calibration |
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from llmcompressor.modifiers.quantization import QuantizationModifier |
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MODEL_ID = "Qwen/Qwen3-30B-A3B" |
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# Load model. |
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model = Qwen3ForCausalLM.from_pretrained(MODEL_ID, dtype="auto") |
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processor = AutoProcessor.from_pretrained(MODEL_ID) |
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model = replace_modules_for_calibration(model) |
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# Configure the quantization algorithm and scheme. |
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# In this case, we: |
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# * quantize the weights to fp8 with per-block quantization |
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# * quantize the activations to fp8 with dynamic token activations |
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recipe = QuantizationModifier( |
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targets="Linear", |
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scheme="FP8_BLOCK", |
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ignore=["lm_head"], |
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) |
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# Apply quantization. |
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oneshot(model=model, recipe=recipe) |
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# Save to disk in compressed-tensors format. |
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SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-block" |
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model.save_pretrained(SAVE_DIR) |
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processor.save_pretrained(SAVE_DIR) |
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``` |
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</details> |
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## Evaluation |
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The model was evaluated on the OpenLLM leaderboard task, using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). |
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[vLLM](https://docs.vllm.ai/en/stable/) was used for all evaluations. |
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<details> |
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<summary>Evaluation details</summary> |
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**Openllm V1** |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="RedHatAI/Qwen3-30B-A3B-FP8-BLOCK",dtype=auto,add_bos_token=True,max_model_len=16384,tensor_parallel_size=2,gpu_memory_utilization=0.9,enable_chunked_prefill=True,trust_remote_code=True \ |
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--tasks openllm \ |
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--write_out \ |
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--batch_size auto \ |
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--show_config |
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``` |
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**Openllm V2** |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="RedHatAI/Qwen3-30B-A3B-FP8-BLOCK",dtype=auto,add_bos_token=False,max_model_len=16384,tensor_parallel_size=2,gpu_memory_utilization=0.7,disable_log_stats=True,enable_chunked_prefill=True,trust_remote_code=True \ |
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--tasks leaderboard \ |
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--apply_chat_template \ |
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--fewshot_as_multiturn \ |
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--write_out \ |
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--batch_size auto \ |
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--show_config |
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``` |
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**Coding Benchmarks** |
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``` |
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evalplus.evaluate --model "RedHatAI/Qwen3-30B-A3B-FP8-BLOCK" \ |
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--dataset "humaneval" \ |
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--backend vllm \ |
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--tp 2 \ |
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--greedy |
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evalplus.evaluate --model "RedHatAI/Qwen3-30B-A3B-FP8-BLOCK" \ |
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--dataset "mbpp" \ |
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--backend vllm \ |
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--tp 2 \ |
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--greedy |
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``` |
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</details> |
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### Accuracy |
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<table> |
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<thead> |
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<tr> |
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<th>Category</th> |
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<th>Metric</th> |
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<th>Qwen/Qwen3-30B-A3B</th> |
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<th>RedHatAI/Qwen3-30B-A3B-FP8-BLOCK</th> |
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<th>Recovery (%)</th> |
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</tr> |
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</thead> |
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<tbody> |
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<!-- OpenLLM Leaderboard V1 --> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V1</b></td> |
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<td>ARC-Challenge (Acc-Norm, 25-shot)</td> |
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<td>69.28</td> |
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<td>69.88</td> |
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<td>100.86</td> |
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</tr> |
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<tr> |
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<td>GSM8K (Strict-Match, 5-shot)</td> |
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<td>89.99</td> |
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<td>89.16</td> |
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<td>99.07</td> |
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</tr> |
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<tr> |
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<td>HellaSwag (Acc-Norm, 10-shot)</td> |
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<td>77.64</td> |
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<td>77.41</td> |
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<td>99.71</td> |
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</tr> |
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<tr> |
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<td>MMLU (Acc, 5-shot)</td> |
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<td>79.50</td> |
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<td>79.37</td> |
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<td>99.84</td> |
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</tr> |
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<tr> |
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<td>TruthfulQA (MC2, 0-shot)</td> |
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<td>53.20</td> |
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<td>53.93</td> |
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<td>101.38</td> |
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</tr> |
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<tr> |
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<td>Winogrande (Acc, 5-shot)</td> |
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<td>72.30</td> |
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<td>72.69</td> |
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<td>100.55</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>73.65</b></td> |
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<td><b>73.74</b></td> |
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<td><b>100.12</b></td> |
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</tr> |
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<!-- OpenLLM Leaderboard V2 --> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V2</b></td> |
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<td>IFEval (Inst Level Strict Acc, 0-shot)</td> |
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<td>48.68</td> |
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<td>47.84</td> |
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<td>98.28</td> |
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</tr> |
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<tr> |
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<td>BBH (Acc-Norm, 3-shot)</td> |
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<td>32.46</td> |
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<td>32.06</td> |
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<td>98.77</td> |
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</tr> |
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<tr> |
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<td>Math-Hard (Exact-Match, 4-shot)</td> |
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<td>18.81</td> |
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<td>18.96</td> |
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<td>100.80</td> |
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</tr> |
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<tr> |
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<td>GPQA (Acc-Norm, 0-shot)</td> |
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<td>24.16</td> |
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<td>24.75</td> |
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<td>102.43</td> |
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</tr> |
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<tr> |
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<td>MUSR (Acc-Norm, 0-shot)</td> |
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<td>38.62</td> |
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<td>40.48</td> |
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<td>104.79</td> |
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</tr> |
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<tr> |
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<td>MMLU-Pro (Acc, 5-shot)</td> |
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<td>23.15</td> |
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<td>25.66</td> |
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<td>110.80</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>30.98</b></td> |
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<td><b>31.62</b></td> |
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<td><b>102.07</b></td> |
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</tr> |
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</tbody> |
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</table> |
