Update README.md

README.md

@@ -5,6 +5,7 @@ language:
   - zh
 base_model:
   - openbmb/MiniCPM5-2B
+base_model_relation: quantized
 pipeline_tag: text-generation
 library_name: litert
 tags:
@@ -18,14 +19,25 @@ tags:
 
 # MiniCPM5-2B (LiteRT-LM)
 
-This repository hosts the [**LiteRT-LM**](https://ai.google.dev/edge/litert-lm) (LiteRT formerly known as TensorFlow Lite) version of **MiniCPM5-2B**, optimized for fully on-device inference on mobile and edge hardware.
+This repository hosts the [**LiteRT-LM**](https://ai.google.dev/edge/litert-lm) (LiteRT formerly known as TensorFlow Lite) version of **[openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B)**, optimized for fully on-device inference on mobile and edge hardware.
 
 ---
 
 ## Available Models
 
-* **`minicpm_wi4c_wi8_afp32.litertlm`**: This model features mixed INT4/INT8 weight-only quantization with FP32 activations (afp32). MLP projections use channelwise INT4 with Hadamard rotation; all remaining weights (attention, embedding, and lmhead) use channelwise INT8.
-* **`minicpm_wi8_afp32.litertlm`**: This model features weight-only INT8 quantization (wi8) with FP32 activations (afp32).
+Recommend to use below both **CPU and GPU compatable** models, originally from [mlboydaisuke/MiniCPM5-2B-LiteRT](https://huggingface.co/mlboydaisuke/MiniCPM5-2B-LiteRT)
+**Requires litert-lm ≥ 0.16** (thought channel + `ThinkingConfig`); measured here on 0.17.0.
+
+| File | Recipe | Size |
+|---|---|---|
+| **`MiniCPM5-2B_int4.litertlm`** | int4 blockwise-32 + OCTAV on linears, int8 embedding | 1.55 GB |
+| **`MiniCPM5-2B_int8.litertlm`** | int8 dynamic on linears + embedding; **fp32 activations declared** (see notes) | 2.60 GB |
+
+The **int4 file is the phone file** (smaller, fastest GPU decode on every platform measured) — best used for direct answers or short reasoning; see the thinking-mode note below. **int8 is the file for reasoning that has to complete**: its thinking chains are ~3–4× shorter than int4's on the same questions and terminate where int4 runs into the token budget. int8's main weight section is 2.33 GB, above the single-section mmap ceiling of default-entitlement iOS apps, so it is a desktop / Android build.
+
+Further, below are some CPU-only models for exploration:
+* `minicpm_wi4c_wi8_afp32.litertlm`: This model features mixed INT4/INT8 weight-only quantization with FP32 activations (afp32). MLP projections use channelwise INT4 with Hadamard rotation; all remaining weights (attention, embedding, and lmhead) use channelwise INT8.
+* `minicpm_wi8_afp32.litertlm`: This model features weight-only INT8 quantization (wi8) with FP32 activations (afp32).
 
 ## What is MiniCPM?
 
@@ -73,7 +85,7 @@ Install `uv` and run the model directly from the LiteRT-LM command line:
 
 ```bash
 uv tool install litert-lm
-uvx litert-lm run --from-huggingface-repo=litert-community/MiniCPM5-2B minicpm_wi4c_wi8_afp32.litertlm --prompt="What is the capital of France?"
+uvx litert-lm run --from-huggingface-repo=litert-community/MiniCPM5-2B MiniCPM5-2B_int4.litertlm --prompt="What is the capital of France?"
 ```
 
 ## Links