Instructions to use litert-community/InternVL3-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/InternVL3-2B 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/InternVL3-2B \ --prompt="Write me a poem"
- LiteRT
How to use litert-community/InternVL3-2B 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
9 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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Card: compatibility line (edge-compat, 2026-09)
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manifest: same-device CPU control rows on the Galaxy S26 for the flagship GPU recommendation (S7 backfill 2026-09-05); the recommendation is rewritten from the measured pair (measured win, kept for prefill, or reversed to cpu)
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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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Card: note the tokenizer-section fix (weights unchanged)
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Replace the tokenizer section with the upstream tokenizer.json (weights, graph and metadata unchanged). The previous SentencePiece conversion of the BPE tokenizer encoded standalone accented/special characters to the wrong ids, turned characters without a vocabulary entry (emoji, some accented capitals) into the end-of-text/end-of-turn token, and could not match the token it had reused as UNK; prompts now tokenize identically to the upstream tokenizer (verified on the LiteRT-LM runtime, see the card note).
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Card: note the default-system-prompt fix (weights unchanged)
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Restore the vendor's default system prompt in the chat template (weights unchanged). The converter's template probe never renders the block the upstream template emits when the caller sends no system message, so the bundle silently dropped it; with no system message the prompt now renders byte-identical to the upstream chat template's output (see the card note).
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Card: correct the start_token note — measured numbers do not carry over
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Card: note the start_token fix (weights unchanged)
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Drop the metadata start_token (weights unchanged)
Verified signatureThe metadata start_token held the literal string "None" (this tokenizer has no BOS), and the engine resolved it to a real vocabulary token - so every prompt began with the word "None", a stream the model was never trained on. Metadata-only change: every tokenizer and TFLite section is byte-identical to the previous file (verified by sha256 per section), so quality and speed are unchanged and the card's measured numbers still stand.402a871
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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.cd1ba8b
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Card: state where each on-device figure actually comes from; replace the iPhone row with a fresh measurement where one was taken
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Card: verified one-command usage, measured Performance table (backends gated on real generation), accuracy note
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Document Gallery 1.0.16 direct Hugging Face import + desktop LiteRT-LM CLI (serve/run)
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Upload README.md with huggingface_hub
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Upload README.md with huggingface_hub
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Upload InternVL3-2B.litertlm with huggingface_hub
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Upload README.md with huggingface_hub
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Upload README.md with huggingface_hub
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Upload InternVL3-2B.litertlm with huggingface_hub
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Upload InternVL3-2B.litertlm with huggingface_hub
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Upload README.md with huggingface_hub
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initial commit
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