@@ -10,6 +10,10 @@ platforms (desktop, mobile, or cloud).
1010[ Common commands] ( #-common-commands ) | 📓 [ Try Colab] ( #-try-colab ) | 🌟
1111[ Quick demos] ( #-quick-demos ) | 🤖 [ Use in coding agent] ( #-use-in-coding-agent )
1212
13+ > [ !NOTE] It's still an early preview under active development, thus has limited
14+ > platform and feature support, plus possible bugs. We appreciate your patience
15+ > and feedback to help us improve it. Welcome issues and PRs!
16+
1317LiteRT CLI is built on top of [ Google AI Edge] ( https://ai.google.dev/edge )
1418stacks, including [ LiteRT] ( https://github.com/google-ai-edge/LiteRT ) ,
1519[ LiteRT-LM] ( https://github.com/google-ai-edge/LiteRT-LM ) ,
@@ -18,10 +22,6 @@ stacks, including [LiteRT](https://github.com/google-ai-edge/LiteRT),
1822[ AI Edge Portal] ( https://ai.google.dev/edge/ai-edge-portal ) , and
1923[ Model Explorer] ( https://ai.google.dev/edge/model-explorer ) .
2024
21- > [ !NOTE] It's still an early preview under active development, thus has limited
22- > platform and feature support, plus possible bugs. We appreciate your patience
23- > and feedback to help us improve it. Welcome issues and PRs!
24-
2525--------------------------------------------------------------------------------
2626
2727## 🚀 Installation
@@ -343,7 +343,34 @@ litert benchmark model.tflite --gcp --device "pixel 7" --gcp-project "your-gcp-p
343343litert benchmark model.tflite --gcp --devices " pixel 7, sm-s931u1" --gpu
344344```
345345
346- ### 7. Visualize a model's architecture
346+ ### 7. Run and benchmark a generative LLM model using LiteRT-LM CLI
347+
348+ ` litert lm ` command will utlitize ` litert-lm ` , and you can use the same command
349+ with ` litert-lm ` , for example, both ` litert lm run ` and ` litert-lm run ` or
350+ ` litert lm benchmark ` and ` litert-lm benchmark ` achieve the same results.
351+
352+ Please follow the
353+ [ LiteRT-LM CLI guide] ( https://ai.google.dev/edge/litert-lm/cli ) for detailed
354+ instructions.
355+
356+ ``` bash
357+ # Run a generative LLM model, and load from hugging face
358+ litert lm run \
359+ --from-huggingface-repo=litert-community/gemma-4-E2B-it-litert-lm \
360+ gemma-4-E2B-it.litertlm \
361+ --prompt=" What is the capital of France?"
362+
363+ # Or load from a local LLM model file
364+ litert lm run ./my_model.litertlm
365+
366+ # Example with a custom prompt
367+ litert lm run ./my_model.litertlm --prompt " Hello, how are you?"
368+
369+ # Benchmark a generative LLM model
370+ litert lm benchmark ./my_model.litertlm
371+ ```
372+
373+ ### 8. Visualize a model's architecture
347374
348375``` bash
349376# Open in Model Explorer graph
@@ -353,7 +380,7 @@ litert visualize model.tflite
353380litert visualize --stop-all
354381```
355382
356- ### 8 . Import a local model
383+ ### 9 . Import a local model
357384
358385``` bash
359386# Import a local file into the centralized cache
@@ -363,7 +390,7 @@ litert import my_model.tflite --model-ref my_model
363390litert import ./my_model_dir --model-ref my_model --hf-id my_org_name/my_model
364391```
365392
366- ### 9 . List managed models
393+ ### 10 . List managed models
367394
368395``` bash
369396# List all managed models
@@ -373,40 +400,12 @@ litert list
373400litert list my_model
374401```
375402
376- ### 10 . Delete a managed model
403+ ### 11 . Delete a managed model
377404
378405``` bash
379406# Delete a model from cache
380407litert delete my_model
381408```
382-
383- ### 11. Run and benchmark a generative LLM model using LiteRT-LM CLI
384-
385- ` litert lm ` command will utlitize ` litert-lm ` , and you can use the same command
386- with ` litert-lm ` , for example, both ` litert lm run ` and ` litert-lm run ` or
387- ` litert lm benchmark ` and ` litert-lm benchmark ` achieve the same results.
388-
389- Please follow the
390- [ LiteRT-LM CLI guide] ( https://ai.google.dev/edge/litert-lm/cli ) for detailed
391- instructions.
392-
393- ``` bash
394- # Run a generative LLM model, and load from hugging face
395- litert lm run \
396- --from-huggingface-repo=litert-community/gemma-4-E2B-it-litert-lm \
397- gemma-4-E2B-it.litertlm \
398- --prompt=" What is the capital of France?"
399-
400- # Or load from a local LLM model file
401- litert lm run ./my_model.litertlm
402-
403- # Example with a custom prompt
404- litert lm run ./my_model.litertlm --prompt " Hello, how are you?"
405-
406- # Benchmark a generative LLM model
407- litert lm benchmark ./my_model.litertlm
408- ```
409-
410409### 12. Clean up all caches
411410
412411``` bash
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