Create README.md

README.md

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+---
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- fp8
+- quantized
+- llm-compressor
+- compressed-tensors
+base_model:
+- Qwen/Qwen3-30B-A3B-Instruct-2507
+---
+
+
+# Qwen3-30B-A3B-Instruct-2507-FP8-dynamic
+
+## Model Overview
+- **Model Architecture:** Qwen3MoeForCausalLM
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Activation quantization:** FP8
+  - **Weight quantization:** FP8
+- **Intended Use Cases:**
+  - Function calling.
+  - Subject matter experts via fine-tuning.
+  - Multilingual instruction following.
+  - Translation.
+
+Quantized version of [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations of [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) 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 of the language model are quantized. 
+
+It's running faster than [Qwen/Qwen3-30B-A3B-Instruct-2507-FP8](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507-FP8) in vLLM/sglang in 4090 or H100, with no diffrence on most benchmarks.
+
+## Deployment
+
+### Use with vLLM
+
+```
+vllm serve bash99/Qwen3-30B-A3B-Instruct-2507-FP8-Dynamic --tensor_parallel_size 2
+```
+
+
+## Creation
+
+This model was quantized using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as shown below.
+
+<details>
+  <summary>Creation details</summary>
+
+```quantize_tofp8.py
+from transformers import AutoProcessor, AutoModelForCausalLM, AutoTokenizer
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+import sys
+
+MODEL_ID = sys.argv[1]
+
+# Load model.
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID, device_map="auto", torch_dtype="auto"
+)
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp8 with per channel via ptq
+#   * quantize the activations to fp8 with dynamic per token
+recipe = QuantizationModifier(targets="Linear",scheme="FP8_DYNAMIC",
+        ignore=["re:.*lm_head", "re:visual.*", 're:.*mlp.gate$', 're:.*mlp.shared_expert_gate$', 're:.*router$']
+    )
+
+# Apply quantization and save to disk in compressed-tensors format.
+SAVE_DIR = MODEL_ID + "-FP8-Dynamic"
+
+oneshot(model=model, recipe=recipe, output_dir=SAVE_DIR)
+processor.save_pretrained(SAVE_DIR)
+
+print(f"========== quantizeing to {SAVE_DIR}, done ==============")
+```
+
+```
+python quantize_tofp8.py Qwen/Qwen3-30B-A3B-Instruct-2507-FP8
+```
+
+</details>