docs: update changelog & quantizer need (#146)

docs/changelog.md

@@ -6,13 +6,22 @@
 - Add EfficientNetB0 model #121
 - Add EfficientNetV2-S model #130
 - Set default target as Warboy's production revision (B0) #125
+- Provide calibration ranges for every model #144
 
 ### Improvements
+- Removed `Quantize` external operators #144
+- Detailed error messages for model file fetching #144
+- ENF generator can do the jobs parallelly #144
+- Removed furiosa.registry dependency #144
 - Faster import for furiosa.models #117
 - Replace yolov5's box decode implementation in Rust #109
 - Remove Cpp postprocessor implementations #102
 - Change packaging tool from setuptools-rust to flit #109
 
+## Removed
+- Truncated models and corresponding postprocesses #144
+- **Breaking:** drop support of directly passing Model to session.create() #144
+
 ### Tasks
 - Release guide for developers #129
 - Report regression test's result with PR comment #110

docs/model_object.md

@@ -50,8 +50,9 @@ To create a session, pass the `enf` field of the model object to the furiosa.run
 
 Passing `Model.source` to `session.create()` allows users to start from source models in ONNX or tflite and customize models to their specific use-cases. This customization includes options such as specifying batch sizes and compiler configurations for optimization purposes. For additional information on Model.source, please refer to [Accessing artifacts and metadata](#accessing_artifacts_and_metadata).
 
-To utilize f32 source models, calibration and quantization are necessary.
+To utilize f32 source models, it is necessary to perform calibration and quantization.
 Pre-calibrated data is readily available in Furiosa-models, facilitating direct access to the quantization process.
+For manual quantization of the model, you can install the `furiosa-quantizer` package, which can be found at this  [package link](https://furiosa-ai.github.io/docs/latest/en/software/python-sdk.html#quantizer).
 The calib_range field of the model class represents this pre-calibrated data.
 After quantization, the output will be in the form of FuriosaAI's IR which can then be passed to the session.
 At this stage, the compiler configuration can be specified.