Instructions to use litert-community/North-Micro-Vision-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/North-Micro-Vision-Instruct 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/North-Micro-Vision-Instruct \ --prompt="Write me a poem"
- LiteRT
How to use litert-community/North-Micro-Vision-Instruct 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
5 days ago
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litertlm_manifest.json: the 2026-10-15 re-packed North-Micro-Vision-Instruct_int4.litertlm and North-Micro-Vision-Instruct_wi8.litertlm (tokenizer section replaced by the upstream tokenizer: sha256 and tokenizer section size) and the digit known_issues items
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Card: 2026-10-15 tokenizer fix (tokenizer section replaced by the upstream tokenizer): 17 times 23 is read as 17 × 23; the tokenizer, 8-question gate and device lines now name the files they describe
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Re-pack North-Micro-Vision-Instruct_wi8.litertlm: tokenizer section replaced by the upstream tokenizer (tokenizer.json of CohereLabs/North-Micro-Vision-Instruct at fa548d9a). The earlier file carried the tokenizer.json that transformers' CohereTokenizer class writes (NFC + per-digit split), so 17 reached the model as 1, 7 and was read as 1.7. Every other section, the LlmMetadata included, is byte-identical.
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Re-pack North-Micro-Vision-Instruct_int4.litertlm: tokenizer section replaced by the upstream tokenizer (tokenizer.json of CohereLabs/North-Micro-Vision-Instruct at fa548d9a). The earlier file carried the tokenizer.json that transformers' CohereTokenizer class writes (NFC + per-digit split), so 17 reached the model as 1, 7 and was read as 1.7. Every other section, the LlmMetadata included, is byte-identical.
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12 days ago
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manifest 0.1.3: capabilities.thinking.control = never (what the bundle's template does about thinking, derived from the template inside the bundle)
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Card: measured device / browser results (edge-compat, 2026-09)
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Add litertlm_manifest.json (deployment manifest 0.1.2: variant selection, verified backends, Android/Apple recommendations, measured rows with conditions)
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Card: base_model_relation: quantized (list under the base model's Quantizations)
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Card: note the chat-template fix for role-'assistant' history (weights unchanged)
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Chat template fix (metadata-only, weights unchanged): the template matched history turns only on role 'model', so a conversation created through the Conversation API with the standard role 'assistant' (e.g. create_conversation(messages=[...]) restoring a chat) silently dropped every assistant turn from the rendered prompt. The role conditions now also accept 'assistant'. Every other section is byte-identical; single-turn renders, token ids, prefill and greedy answers are unchanged (verified on the LiteRT-LM runtime). See google-ai-edge/LiteRT-LM#3417.
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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.5b8ffed
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card: on-device demo table (Pixel 8a answers on two HF sample photos)
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card: iPhone 17 Pro re-gate on the published file (vision probes 2/2, peak 2.9-3.0 GB, TTFT 2.3-5.0 s) + Core AI same-device paragraph
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int4 blockwise-32 decoder variant + int8 vision
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wi8 decoder (fp32_fp16 hint) + int8 vision, static-512 fast_vlm
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North-Micro-Vision-Instruct LiteRT-LM card
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initial commit
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