Instructions to use litert-community/DeepSeek-R1-Distill-Qwen-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/DeepSeek-R1-Distill-Qwen-1.5B with LiteRT-LM:
# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM) # and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter). # For platform-specific integration guides, please refer to the official developer website: # https://ai.google.dev/edge/litert-lm # To try LiteRT-LM, the easiest way is to use our CLI tool. # 1. Install the LiteRT-LM CLI tool: pip install -U litert-lm # 2. Download and run this model locally: # See: https://ai.google.dev/edge/litert-lm/cli litert-lm run \ --from-huggingface-repo=litert-community/DeepSeek-R1-Distill-Qwen-1.5B \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -5,6 +5,8 @@ pipeline_tag: text-generation
|
|
| 5 |
library_name: litert-lm
|
| 6 |
tags:
|
| 7 |
- chat
|
|
|
|
|
|
|
| 8 |
---
|
| 9 |
|
| 10 |
# litert-community/DeepSeek-R1-Distill-Qwen-1.5B
|
|
@@ -99,4 +101,4 @@ Note that all benchmark stats are from a Samsung S24 Ultra with
|
|
| 99 |
[XNNPACK](https://github.com/google/XNNPACK) delegate with 4 threads
|
| 100 |
* Benchmark is done assuming XNNPACK cache is enabled
|
| 101 |
* Benchmark is run with cache enabled and initialized. During the first run, the time to first token may differ.
|
| 102 |
-
* dynamic_int8: quantized model with int8 weights and float activations.
|
|
|
|
| 5 |
library_name: litert-lm
|
| 6 |
tags:
|
| 7 |
- chat
|
| 8 |
+
language:
|
| 9 |
+
- th
|
| 10 |
---
|
| 11 |
|
| 12 |
# litert-community/DeepSeek-R1-Distill-Qwen-1.5B
|
|
|
|
| 101 |
[XNNPACK](https://github.com/google/XNNPACK) delegate with 4 threads
|
| 102 |
* Benchmark is done assuming XNNPACK cache is enabled
|
| 103 |
* Benchmark is run with cache enabled and initialized. During the first run, the time to first token may differ.
|
| 104 |
+
* dynamic_int8: quantized model with int8 weights and float activations.
|