Create README.md

README.md

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+---
+tags:
+- w4a16
+- int4
+- vllm
+- audio
+license: apache-2.0
+license_link: https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
+language:
+  - en
+base_model: openai/whisper-large-v3
+library_name: transformers
+---
+
+# whisper-large-v3-quantized.w4a16
+
+## Model Overview
+- **Model Architecture:** whisper-large-v3
+  - **Input:** Audio-Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT4
+  - **Activation quantization:** FP16
+- **Release Date:** 1/31/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) to INT4 data type, ready for inference with vLLM >= 0.5.2.
+
+## Deployment
+
+### 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 vllm.assets.audio import AudioAsset
+from vllm import LLM, SamplingParams
+
+# prepare model
+llm = LLM(
+    model="neuralmagic/whisper-large-v3.w4a16",
+    max_model_len=448,
+    max_num_seqs=400,
+    limit_mm_per_prompt={"audio": 1},
+)
+
+# prepare inputs
+inputs = {  # Test explicit encoder/decoder prompt
+    "encoder_prompt": {
+        "prompt": "",
+        "multi_modal_data": {
+            "audio": AudioAsset("winning_call").audio_and_sample_rate,
+        },
+    },
+    "decoder_prompt": "<|startoftranscript|>",
+}
+
+# generate response
+print("========== SAMPLE GENERATION ==============")
+outputs = llm.generate(inputs, SamplingParams(temperature=0.0, max_tokens=64))
+print(f"PROMPT  : {outputs[0].prompt}")
+print(f"RESPONSE: {outputs[0].outputs[0].text}")
+print("==========================================")
+```
+
+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 as part a multimodal announcement blog.
+
+```python
+import torch
+from datasets import load_dataset
+from transformers import WhisperProcessor
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot
+from llmcompressor.transformers.tracing import TraceableWhisperForConditionalGeneration
+
+# Select model and load it.
+MODEL_ID = "openai/whisper-large-v3"
+
+model = TraceableWhisperForConditionalGeneration.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+    torch_dtype="auto",
+)
+model.config.forced_decoder_ids = None
+processor = WhisperProcessor.from_pretrained(MODEL_ID)
+
+# Configure processor the dataset task.
+processor.tokenizer.set_prefix_tokens(language="en", task="transcribe")
+
+# Select calibration dataset.
+DATASET_ID = "MLCommons/peoples_speech"
+DATASET_SUBSET = "test"
+DATASET_SPLIT = "test"
+
+# Select number of samples. 512 samples is a good place to start.
+# Increasing the number of samples can improve accuracy.
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 2048
+
+# Load dataset and preprocess.
+ds = load_dataset(
+    DATASET_ID,
+    DATASET_SUBSET,
+    split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]",
+    trust_remote_code=True,
+)
+
+
+def preprocess(example):
+    return {
+        "array": example["audio"]["array"],
+        "sampling_rate": example["audio"]["sampling_rate"],
+        "text": " " + example["text"].capitalize(),
+    }
+
+
+ds = ds.map(preprocess, remove_columns=ds.column_names)
+
+
+# Process inputs.
+def process(sample):
+    inputs = processor(
+        audio=sample["array"],
+        sampling_rate=sample["sampling_rate"],
+        text=sample["text"],
+        add_special_tokens=True,
+        return_tensors="pt",
+    )
+
+    inputs["input_features"] = inputs["input_features"].to(dtype=model.dtype)
+    inputs["decoder_input_ids"] = inputs["labels"]
+    del inputs["labels"]
+
+    return inputs
+
+
+ds = ds.map(process, remove_columns=ds.column_names)
+
+
+# Define a oneshot data collator for multimodal inputs.
+def data_collator(batch):
+    assert len(batch) == 1
+    return {key: torch.tensor(value) for key, value in batch[0].items()}
+
+
+# Recipe
+recipe = GPTQModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"])
+
+# Apply algorithms.
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    data_collator=data_collator,
+)
+
+# Confirm generations of the quantized model look sane.
+print("\n\n")
+print("========== SAMPLE GENERATION ==============")
+sample_features = next(iter(ds))["input_features"]
+sample_decoder_ids = [processor.tokenizer.prefix_tokens]
+sample_input = {
+    "input_features": torch.tensor(sample_features).to(model.device),
+    "decoder_input_ids": torch.tensor(sample_decoder_ids).to(model.device),
+}
+
+output = model.generate(**sample_input, language="en")
+print(processor.batch_decode(output, skip_special_tokens=True))
+print("==========================================\n\n")
+# that's where you have a lot of windows in the south no actually that's passive solar
+# and passive solar is something that was developed and designed in the 1960s and 70s
+# and it was a great thing for what it was at the time but it's not a passive house
+
+# Save to disk compressed.
+SAVE_DIR = MODEL_ID.split("/")[1] + "-W4A16-G128"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+processor.save_pretrained(SAVE_DIR)
+```
+
+
+## Evaluation
+Base Model
+```
+Total Test Time: 94.4606 seconds
+Total Requests: 511
+Successful Requests: 511
+Average Latency: 53.3529 seconds
+Median Latency: 52.7258 seconds
+95th Percentile Latency: 86.5851 seconds
+Estimated req_Throughput: 5.41 requests/s
+Estimated Throughput: 100.79 tok/s
+WER: 12.660815197787665
+```
+
+W4A16
+```
+Total Test Time: 106.2064 seconds
+Total Requests: 511
+Successful Requests: 511
+Average Latency: 59.7467 seconds
+Median Latency: 58.3930 seconds
+95th Percentile Latency: 97.4831 seconds
+Estimated req_Throughput: 4.81 requests/s
+Estimated Throughput: 89.35 tok/s
+WER: 12.949380786341228
+```
+
+### BibTeX entry and citation info
+```bibtex
+@misc{radford2022whisper,
+  doi = {10.48550/ARXIV.2212.04356},
+  url = {https://arxiv.org/abs/2212.04356},
+  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
+  title = {Robust Speech Recognition via Large-Scale Weak Supervision},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}