MiniCPM5-1B packaged in the .litertlm format so it runs fully on-device — Android, iOS, Desktop and IoT — with the LiteRT-LM framework. No internet connection required at inference time.

👉 Not sure which file to pick? Download `MiniCPM5-1B.litertlm`

That's the one and only file here, and it's meant to "just work". You don't need to understand quantization to use it.

File	What it is	Who it's for
`MiniCPM5-1B.litertlm`	The ready-to-run model (INT8, ~1.1 GB)	Everyone — this is the default
`chat_template.jinja`	The chat format (auto-used by the runtime)	Advanced / reference

Try it in one command (Desktop)

bash

uv tool install litert-lm
uvx litert-lm run --from-huggingface-repo=luispoveda93/MiniCPM5-1B MiniCPM5-1B.litertlm --prompt="What is the capital of France?"

Run it on your phone (Android)

Install the Google AI Edge Gallery app.
Open Model manager → “+” → Import from HF, paste this repo's link, and download MiniCPM5-1B.litertlm.

See the Edge Gallery wiki for details.

About this model

MiniCPM5-1B is a compact 1B decoder-only model from OpenBMB (standard LlamaForCausalLM), built for local assistants, coding agents, and tool use, with a hybrid <think> chat mode and long-context support.

Item	Value
Parameters	1,080,632,832 (~1B)
Layers	24
Attention (GQA)	16 query heads / 2 KV heads
Context length	131,072
Quantization	INT8 dynamic-range (weights), FP32 activations
Signatures	`prefill_128`, `prefill_512`, `decode` (KV-cache 1280)

Recommended sampling (from the source model card)

Mode	Params	Enable
Think	`temperature=0.9, top_p=0.95`	`enable_thinking=True`
No Think	`temperature=0.7, top_p=0.95`	`enable_thinking=False`

How this was built (transparency)

Converted from the BF16 source (openbmb/MiniCPM5-1B) with Google AI Edge tooling:

Re-authoring + TFLite conversion: ai-edge-torch Generative API. MiniCPM5-1B maps onto the standard decoder-only building blocks (hidden 1536, 24 layers, GQA 16/2, head_dim 128, RMSNorm 1e-6, RoPE θ=5e6, untied lm_head, vocab 130560).
Quantization: INT8 dynamic-range weight-only.
Packaging: litert-lm-builder, native minicpm5 runtime type, source Hugging Face tokenizer, start token <s>, stop tokens [1, 130073] (</s>, <|im_end|>), thought reasoning channel, and an ExecutorMetadata section declaring all 48 KV-cache state buffers (required for on-device prefill).
Verification: the re-authored model was checked against the original transformers implementation — last-token logits matched with cosine 1.0000, identical argmax, 5/5 top-5 overlap; parameter count matches exactly. The packaged bundle was also run end-to-end with litert-lm run and generates correctly (reasoning channel parses as expected).

This is a personal community contribution, not an official OpenBMB or Google release. An INT4 (wi4b32) variant with a smaller footprint is available from the official litert-community/MiniCPM5-1B repo.

Community-Contributed Evaluation: Spanish, Catalan, Basque, Galician (0-shot)

⚠️ Note: This section is a community addition — it does not appear in the original openbmb/MiniCPM5-1B model card. It was benchmarked independently by this repo's maintainer and is provided as supplementary information, not an official OpenBMB result.

To understand how this compact 1B EN/ZH-pretrained model performs on Spain's co-official languages, we ran the exact task selection published on the Salamandra-2b-instruct model card — a 2.25B model whose pretraining specifically oversampled these languages 2x — using the same LM Evaluation Harness task suite (SpanishBench, CatalanBench, BasqueBench, GalicianBench), 0-shot, chat template applied. Evaluation was run on the BF16 source model (openbmb/MiniCPM5-1B), not the on-device .litertlm build in this repo. The Salamandra-2B column is transcribed verbatim from its published card and was not re-run.

Spanish

Category	Task	Metric	MiniCPM5-1B	Salamandra-2B
Commonsense Reasoning	xstorycloze_es	acc	51.69	61.95
NLI	wnli_es	acc	43.66	49.30
xnli_es	acc	42.49	48.52
Paraphrasing	paws_es	acc	57.30	57.10
QA	xquad_es	f1^†	0.00	29.60
Translation	flores_es	bleu	0.00	15.92

^† Salamandra's card lists this task under metric "acc"; the installed xquad_es task only computes f1/exact_match, so f1 is reported here for MiniCPM5-1B.

Catalan

Category	Task	Metric	MiniCPM5-1B	Salamandra-2B
Commonsense Reasoning	copa_ca	acc	50.80	71.00
xstorycloze_ca	acc	48.64	61.61
NLI	wnli_ca	acc	54.93	56.34
xnli_ca	acc	37.83	52.54
Paraphrasing	parafraseja	acc	56.75	64.48
paws_ca	acc	56.45	60.30
QA	arc_ca_easy	acc	28.87	52.15
arc_ca_challenge	acc	20.22	30.03
openbookqa_ca	acc	21.80	29.20
piqa_ca	acc	53.32	63.93
siqa_ca	acc	33.76	42.32
Translation	flores_ca	bleu	0.00	20.04

Basque

Category	Task	Metric	MiniCPM5-1B	Salamandra-2B
Commonsense Reasoning	xcopa_eu	acc	48.20	55.20
xstorycloze_eu	acc	49.77	55.26
NLI	wnli_eu	acc	52.11	52.11
xnli_eu	acc	33.91	45.89
QA	eus_exams^‡	acc	24.40	24.94
eus_proficiency	acc	24.28	25.15
eus_trivia	acc	30.44	27.35
Reading Comprehension	eus_reading	acc	25.85	28.69
Translation	flores_eu	bleu	0.00	7.93

^‡ eus_exams is a group of 29 exam sub-tasks; MiniCPM5-1B's score is the sample-count-weighted average across all sub-tasks.

Galician

Category	Task	Metric	MiniCPM5-1B	Salamandra-2B
Paraphrasing	parafrases_gl	acc	52.72	53.40
paws_gl	acc	53.95	56.85
QA	openbookqa_gl	acc	21.60	26.20
Translation	flores_gl	bleu	0.00	17.53

Bold values indicate MiniCPM5-1B matches or exceeds the Salamandra-2B-instruct published baseline despite the parameter and pretraining-data gap. MiniCPM5-1B has no targeted Spanish/Catalan/Basque/Galician pretraining data; the gaps on Basque and Galician (and all FLORES/BLEU translation tasks, which scored ≈0 across every language) are the expected result of that mismatch, not a defect in the on-device conversion. This comparison is offered as context for anyone considering MiniCPM5-1B for these languages — it is not a claim of official parity with Salamandra.

License

Apache-2.0, following the source model.

luispoveda93/MiniCPM5-1B

MiniCPM5-1B — LiteRT-LM (on-device)

👉 Not sure which file to pick? Download MiniCPM5-1B.litertlm