Instructions to use mlboydaisuke/DeepSeek-R1-Distill-Qwen-7B-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use mlboydaisuke/DeepSeek-R1-Distill-Qwen-7B-LiteRT 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 # A single .litertlm file in the repo is picked automatically; otherwise the CLI asks which one to run # (or pass its name right after the repo id). litert-lm run \ --from-huggingface-repo=mlboydaisuke/DeepSeek-R1-Distill-Qwen-7B-LiteRT \ --prompt="Write me a poem"
- LiteRT
How to use mlboydaisuke/DeepSeek-R1-Distill-Qwen-7B-LiteRT with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Commit History
10 days ago
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Card: the 2026-10-14 re-pack declares the thought channel (metadata only), so the reasoning arrives on the thought channel; a small max_output_tokens leaves the answer text empty while the model is still reasoning
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Re-pack model.litertlm: declare the thought channel in LlmMetadata (channel_name thought, <think> ... </think>; metadata only, every other section byte-identical; litert-lm returns the reasoning on the thought channel instead of inside the message text)
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Card: base_model_relation: quantized (so the conversion lists under the base model's Quantizations, not Finetunes)
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Link the card back to its collection and the request box
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Add desktop LiteRT-LM CLI section (import/run/serve)
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Upload model.litertlm with huggingface_hub
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Upload README.md with huggingface_hub
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initial commit
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