Create README.md

README.md

@@ -1,4 +1,150 @@
 ---
-base_model:
-- Qwen/QwQ-32B
----
+license: mit
+tags:
+- qwen
+- qwq
+- fp8
+- vllm
+base_model: Qwen/QwQ-32B
+library_name: transformers
+---
+
+# QwQ-32B-FP8-dynamic
+
+## Model Overview
+- **Model Architecture:** Qwen2ForCausalLM
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Release Date:** 3/6/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B).
+
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations of [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) 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](https://github.com/vllm-project/llm-compressor) is used for quantization.
+
+
+## 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 transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+number_gpus = 1
+model_name = "neuralmagic/QwQ-32B-FP8-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](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. 
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.transformers import oneshot
+import os
+
+# Load model
+model_stub = "Qwen/QwQ-32B"
+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}")
+```
+
+
+### Accuracy
+
+<table>
+  <thead>
+    <tr>
+      <th>Category</th>
+      <th>Metric</th>
+      <th>Qwen/QwQ-32B</th>
+      <th>neuralmagic/QwQ-32B-FP8-dynamic</th>
+      <th>Recovery</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+<td rowspan="4"><b>Reasoning</b></td>
+<td>AIME 2024 (pass@1)</td> 78.66, 97.39, 64.72; 79.50, 97.44, 63.21
+<td>78.66</td>
+<td>79.40</td>
+<td>100.94%</td>
+</tr>
+<tr>
+<td>MATH-500 (pass@1)</td>
+<td>97.39</td>
+<td>97.44</td>
+<td>100.05%</td>
+</tr>
+<tr>
+<td>GPQA Diamond (pass@1)</td>
+<td>64.72</td>
+<td>63.21</td>
+<td>97.66%</td>
+</tr>
+<tr>
+<td><b>Average Score</b></td>
+<td><b>80.25</b></td>
+<td><b>80.05</b></td>
+<td><b>99.75%</b></td>
+</tr>
+    
+  </tbody>
+</table>