Create README.md

README.md

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+---
+tags:
+- w8a8
+- INT8
+- 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-tiny
+library_name: transformers
+---
+
+# whisper-tiny-quantized.w8a8
+
+## Model Overview
+- **Model Architecture:** whisper-tiny
+  - **Input:** Audio-Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT8
+  - **Activation quantization:** INT8
+- **Release Date:** 04/16/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) to INT8 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-tiny-quantized.w8a8",
+    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. 
+
+<details>
+  <summary>Model Creation Code</summary>
+
+```bash
+python quantize.py --model_path openai/whisper-tiny --quant_path "output_dir/whisper-tiny-quantized.w8a8" --calib_size 1024 --dampening_frac 0.01
+```
+
+
+```python
+import torch
+import argparse
+from datasets import load_dataset
+from transformers import WhisperProcessor
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers.tracing import TraceableWhisperForConditionalGeneration
+import os
+from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy, ActivationOrdering, QuantizationScheme
+from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model_path', type=str)
+parser.add_argument('--quant_path', type=str)
+parser.add_argument('--calib_size', type=int, default=256)
+parser.add_argument('--dampening_frac', type=float, default=0.1) 
+parser.add_argument('--observer', type=str, default="minmax")
+parser.add_argument('--save_dir', type=str, required=True)
+
+
+args = parser.parse_args()
+model_id = args.model_path
+
+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 for calibration. 512 samples is a good place to start.
+# Increasing the number of samples can improve accuracy.
+
+NUM_CALIBRATION_SAMPLES = args.calib_size
+MAX_SEQUENCE_LENGTH = 2048
+dampening_frac=args.dampening_frac
+actorder_arg=args.actorder
+group_size=args.group_size
+
+# 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()}
+
+ignore=["lm_head"]
+
+#Recipe
+recipe = [
+    GPTQModifier(
+        targets="Linear",
+        scheme="W8A8",
+        sequential_targets=["WhisperEncoderLayer", "WhisperDecoderLayer"],
+        ignore=ignore,
+    )
+]
+
+# 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,
+)
+
+
+# Save to disk compressed.
+save_name = f"{model_id.split('/')[-1]}-quantized.w8a8"
+save_path = os.path.join(args.save_dir, save_name)
+print("Saving model:", save_path)
+model.save_pretrained(save_path, save_compressed=True)
+processor.save_pretrained(save_path)
+```
+</details>
+
+## Evaluation
+
+The model was evaluated on [LibriSpeech](https://huggingface.co/datasets/lmms-lab/librispeech) and [Fleurs](https://huggingface.co/datasets/lmms-lab/fleurs) datasets using [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval), via the following commands:
+
+<details>
+<summary>Evaluation Commands</summary>
+  
+Librispeech:
+```
+lmms-eval \
+    --model=whisper_vllm \
+    --model_args="pretrained=neuralmagic-ent/whisper-tiny-quantized.w8a8" \
+    --batch_size 64 \
+    --output_path <output_file_path> \
+    --tasks librispeech
+```
+
+Fleurs:
+```
+lmms-eval \
+    --model=whisper_vllm \
+    --model_args="pretrained=neuralmagic-ent/whisper-tiny-quantized.w8a8" \
+    --batch_size 64 \
+    --output_path <output_file_path> \
+    --tasks fleurs
+```
+</details>
+
+<table>
+  <thead>
+    <tr>
+      <th>Benchmark</th>
+      <th>Split</th>
+      <th>BF16</th>
+      <th>w8a8</th>
+      <th>Recovery (%)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td rowspan="2"><b>LibriSpeech (WER)</b></td>
+      <td>test-clean</td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr>
+      <td>test-other</td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr>
+      <td rowspan="3"><b>Fleurs (X→en, BLEU)</b></td>
+      <td>cmn_hans_cn</td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr>
+      <td>en</td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr>
+      <td>yue_hant_hk</td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+  </tbody>
+</table>