Instructions to use litert-community/DeepSeek-R1-Distill-Qwen-7B 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-7B 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=litert-community/DeepSeek-R1-Distill-Qwen-7B \ --prompt="Write me a poem"
- LiteRT
How to use litert-community/DeepSeek-R1-Distill-Qwen-7B 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
Yesterday
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Card: add a note on litert-lm's 10-minute send_message timeout
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2 days ago
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litertlm_manifest.json: the externalized file's sha256, size and sections; min runtime 0.19.0 and the Android GPU known issue (LiteRT-LM#3986)
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Move the externalized file from the web branch to main (TFLiteWeights sections; Android GPU needs LiteRT-LM 0.19.0 or later)
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7 days ago
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Add known runtime issues: the second-turn stop token and the final mark before LiteRT-LM 0.17.0
Verified signatureAdds the lines under Known runtime issues; nothing else in the card changes.c024210
12 days ago
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Say that the CLI's Init counts engine creation twice (LiteRT-LM #4057)
Verified signatureThe CLI's printed Init includes both Init Total and the phases inside it. One sentence, no number changed.9493a41
25 days ago
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manifest 0.1.3: capabilities.thinking.control = model (what the bundle's template does about thinking, derived from the template inside the bundle)
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26 days ago
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Card: measured device / browser results (edge-compat, 2026-09)
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27 days ago
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GPU graph re-ship: KV-cache update and attention rewritten into the runtime's GPU composite ops plus a 16-token prefill signature; weights, tokenizer and metadata byte-identical; CPU output bit-identical (card and manifest updated)
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Card: measured device / browser results (edge-compat, 2026-09)
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Card: compatibility line (edge-compat, 2026-09)
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Card: definition line (LiteRT, 2026-09)
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manifest: Galaxy S26 GPU rows from the S4 gate sweep (Android recommendation where none existed)
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Card: base_model_relation: quantized (list under the base model's Quantizations)
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Add measured Raspberry Pi 5 (CPU) section
Verified signatureNumbers from the 2026-09 Raspberry Pi 5 LLM sweep: litert-lm benchmark 0.16.1, CPU backend, 4 threads, 256 prefill + 256 decode tokens, compile cache disabled, 3 invocations per file with cooldown, thermal throttle state verified 0x0 on every run, and a real-generation gate on the same backend before any number was recorded. Median + spread, never single-shot.6e57ddd
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Manifest: regenerate after the thought-channel re-pack (sha256 + capabilities.thinking)
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Card: note the thought-channel declaration (metadata only, weights unchanged)
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Declare the thought channel so the reasoning is separable and the thinking budget applies (metadata only; weights byte-identical)
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Add measured Galaxy S26 GPU backend section
Verified signatureFunctional verdict from the S26 GPU gate. Primary log: litertlm-convert/community_accel_work/s4_gpu_gate/results.jsonl. Delegation counts and peak RSS are read from that run's own log; throughput is deliberately absent, because an Android GPU speed is not quotable without a CPU row from the same phone and that control has not been run.4ca77d5
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Add litertlm_manifest.json — machine-readable deployment manifest (variant selection, backend recommendations, measured performance)
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Card: measured Performance table (M4 Max CPU/GPU + on-device where measured), accuracy note
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docs: add Training data & PII section (submission-guideline compliance)
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Upload folder using huggingface_hub
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initial commit
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