fix py script

Author: JyotinderSingh

guides/ipynb/quantization/overview.ipynb

@@ -73,12 +73,12 @@
     "\n",
     "## Quantizing Keras Models\n",
     "\n",
-    "Quantization is applied explicitly after layers or models are built. The API is designed to be predictable: you call quantize, the graph is rewritten,the weights are replaced, and you can immediately run inference or save the model.\n",
+    "Quantization is applied explicitly after layers or models are built. The API is designed to be predictable: you call quantize, the graph is rewritten, the weights are replaced, and you can immediately run inference or save the model.\n",
     "\n",
     "Typical workflow:\n",
     "\n",
     "1. **Build / load your FP model.** Train if needed. Ensure `build()` or a forward pass has materialized weights.\n",
-    "2. **(GPTQ only)** Keras may run a short calibration pass to collect activation ranges (you can pass a small, representative dataset).\n",
+    "2. **(GPTQ only)** For GPTQ, Keras runs a short calibration pass to collect activation statistics. You will need to provide a small, representative dataset for this purpose.\n",
     "3. **Invoke quantization.** Call `model.quantize(\"<mode>\")` or `layer.quantize(\"<mode>\")` with `\"int8\"`, `\"int4\"`, `\"float8\"`, or `\"gptq\"` (weight-only).\n",
     "4. **Use or save.** Run inference, or `model.save(...)`. Quantization state (packed weights, scales, metadata) is preserved on save/load.\n",
     "\n",

guides/md/quantization/overview.md

@@ -71,12 +71,12 @@ Keras currently focuses on the following numeric formats. Each mode can be appli
 
 ## Quantizing Keras Models
 
-Quantization is applied explicitly after layers or models are built. The API is designed to be predictable: you call quantize, the graph is rewritten,the weights are replaced, and you can immediately run inference or save the model.
+Quantization is applied explicitly after layers or models are built. The API is designed to be predictable: you call quantize, the graph is rewritten, the weights are replaced, and you can immediately run inference or save the model.
 
 Typical workflow:
 
 1. **Build / load your FP model.** Train if needed. Ensure `build()` or a forward pass has materialized weights.
-2. **(GPTQ only)** Keras may run a short calibration pass to collect activation ranges (you can pass a small, representative dataset).
+2. **(GPTQ only)** For GPTQ, Keras runs a short calibration pass to collect activation statistics. You will need to provide a small, representative dataset for this purpose.
 3. **Invoke quantization.** Call `model.quantize("<mode>")` or `layer.quantize("<mode>")` with `"int8"`, `"int4"`, `"float8"`, or `"gptq"` (weight-only).
 4. **Use or save.** Run inference, or `model.save(...)`. Quantization state (packed weights, scales, metadata) is preserved on save/load.
 

guides/quantization/overview.py

@@ -63,12 +63,12 @@
 
 ## Quantizing Keras Models
 
-Quantization is applied explicitly after layers or models are built. The API is designed to be predictable: you call quantize, the graph is rewritten,the weights are replaced, and you can immediately run inference or save the model.
+Quantization is applied explicitly after layers or models are built. The API is designed to be predictable: you call quantize, the graph is rewritten, the weights are replaced, and you can immediately run inference or save the model.
 
 Typical workflow:
 
 1. **Build / load your FP model.** Train if needed. Ensure `build()` or a forward pass has materialized weights.
-2. **(GPTQ only)** Keras may run a short calibration pass to collect activation ranges (you can pass a small, representative dataset).
+2. **(GPTQ only)** For GPTQ, Keras runs a short calibration pass to collect activation statistics. You will need to provide a small, representative dataset for this purpose.
 3. **Invoke quantization.** Call `model.quantize("<mode>")` or `layer.quantize("<mode>")` with `"int8"`, `"int4"`, `"float8"`, or `"gptq"` (weight-only).
 4. **Use or save.** Run inference, or `model.save(...)`. Quantization state (packed weights, scales, metadata) is preserved on save/load.