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We are releasing MiniCPM5-1B, the first model in the MiniCPM5 series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA.
🏆 1B-class open-source SOTA: compared with strong open-source models in the same size class, MiniCPM5-1B reaches SOTA within this comparison set. Its advantage is most visible in agentic tool use, code generation, and difficult reasoning.
🧠 Hybrid Reasoning: built-in <think> chat template, switch via enable_thinking. The same checkpoint serves as both a fast assistant and a deliberate reasoner.
🛠️ Deployment / Fine-tuning Resources: the MiniCPM GitHub repo provides single-page cookbooks and Agent Skills for major inference backends and fine-tuning frameworks.
🐱 Desktop Pet: a local-LLM desktop pet driven by MiniCPM5-1B.
Use this directory to choose the model format that matches your runtime:
MiniCPM5-1B has the following features:
LlamaForCausalLMMiniCPM5-1B is the first checkpoint in the MiniCPM5 series. It is designed for local assistants, coding agents, tool-use workflows, and reasoning scenarios where a compact model is preferred. The model keeps a small deployment footprint while providing native long-context support and both Think / No Think chat modes through the same checkpoint.
We compare MiniCPM5-1B with strong open-source models in the same size class, including LFM2.5-1.2B-Thinking, Qwen3-0.6B/think and Qwen3.5-0.8B/think. These are capable baselines; within this comparison set, MiniCPM5-1B reaches 1B-class open-source SOTA, with its advantage most visible in tool use, code generation, and difficult reasoning. This makes it a practical choice for local coding agents, tool assistants, and reasoning assistants.
The training of MiniCPM5-1B is a full-stack practice of UltraData Tiered Data Management, covering three stages: base training, mid-training, and post-training.
During base training, the model goes through stable training and decay training to build core language capability and training stability. It then enters mid-training to further strengthen target capabilities and adapt to the target data distribution. The training corpus is released alongside the model as Ultra-FineWeb, Ultra-FineWeb-L3, and UltraData-Math.
During post-training, we proceed in three steps: SFT, RL, and OPD. We first use 200B tokens of deep-thinking SFT and 200B tokens of hybrid-thinking SFT to establish deep-thinking, hybrid-thinking, and general chat abilities; the SFT data is released as UltraData-SFT-2605. We then train specialized RL teachers for math, code, closed-book QA, writing, and related domains, and use On-Policy Distillation (OPD) to distill these teachers back into one release model.
RL + OPD is a key part of MiniCPM5-1B post-training. On math, code and instruction-following tasks, RL + OPD raises the average score by ↑16 points while cutting the share of responses that hit the max-tokens budget by ↓29 percentage points. The figures below show the two-stage Reasoning RL pipeline, score gains, and the drop in overlong responses.
RL combines complementary training signals for reasoning, closed-book QA, writing, instruction following, long-context understanding, and general dialogue. Reasoning RL is based on DAPO-Math-17k, follows the minimalist recipe of JustRL, and further adds a two-stage length schedule to reduce overlong responses while improving reasoning accuracy. We also use TriviaQA, NQ-Open, LongWriter-Zero-RLData, synthesized verifiable RLVR data, and pair-wise RLHF signals to improve reliability, instruction following, and user experience.
OPD builds on Thinking Machines Lab's On-Policy Distillation and incorporates implementation improvements from Rethinking On-Policy Distillation. In the RL framework, we use reverse KL divergence as the advantage estimate, replacing the original verification-based advantage. At each response position, we take top-k logits from both the student and teacher models, compute reverse KL on the union of the two token sets, and balance the accuracy of the RKL signal with training efficiency. OPD reuses the in-domain prompts used to train each RL teacher as distillation data, so no additional data curation is required.
Recommended chat template sampling:
For tool / function calling, SGLang is the recommended backend. MiniCPM5-1B emits XML-style tool calls and SGLang's built-in minicpm5 parser converts them to OpenAI-compatible tool_calls natively:
MiniCPM5-1B uses the standard LlamaForCausalLM architecture, so mainstream inference engines can load it directly: no custom kernels, no model-code fork. For step-by-step deployment and fine-tuning instructions, use the GitHub cookbooks below. Agent Skills are linked as GitHub resources for users working with Cursor / Claude Code style coding agents.
In addition to the deployment and fine-tuning frameworks listed above, MiniCPM5-1B is also supported by FlagOS for multi-chip deployment.
To enable large-scale deployment across different AI chips, Beijing Zhiyuan Research Institute, together with numerous research institutions, chip manufacturers, system vendors, and algorithm and software organizations both domestically and internationally, jointly initiated and established the FlagOS Open Source Community.
The FlagOS community is dedicated to building a unified, open-source system software stack for various AI chips, encompassing core open-source projects such as a large-scale operator library, a unified AI compiler, parallel training and inference frameworks, and a unified communication library. It aims to create an open technology ecosystem connecting the “model-system-chip” layers. By enabling “develop once, deploy across chips”, FlagOS unlocks the computational potential of hardware, breaks down the ecosystem silos between different chip software stacks, and effectively reduces migration costs for developers.The FlagOS community fosters an AI hardware and software ecosystem, overcomes single-vendor closed-source monopolies, promotes widespread deployment of AI hardware technologies, and is committed to rooted in China while embracing global collaboration.
Official website express: https://flagos.io
Thanks to FlagOS’s unified multi-chip AI system software stack, MiniCPM5-1B was adapted to 4–5 different AI chips in an extremely short time. Currently, the multi-chip version of MiniCPM5-1B has been released on FlagRelease, FlagOS’s platform for automatic migration, adaptation, and deployment of large models across multi-architecture AI chips. Details are as follows:
FlagRelease is a platform developed by the FlagOS team for automatic migration, adaptation, and deployment of large models across multi-architecture AI chips. The multi-chip version of MiniCPM5-1B has already been released on FlagRelease. All necessary software packages are pre-installed on the platform, so users do not need to install anything.
Official Repository: https://github.com/flagos-ai/FlagGems
You can enable flagGems acceleration by adding the import of flagGems in the source code of vllm where inference is performed.
vllm-plugin-FL is a plugin built for the vLLM inference/service framework. Developed on top of FlagOS’s unified multi-chip backend, it is designed to extend vLLM’s capabilities and performance across a variety of hardware environments.
We also ship OpenBMB/MiniCPM-Desk-Pet, a desktop pet driven locally by MiniCPM5-1B. It supports Apple Silicon / NVIDIA GPU / CPU paths, can work with coding agents such as Cursor, Claude Code, and Codex, and supports LoRA persona switching.
MiniCPM5-1B is a language model that generates content based on learned statistical patterns from training data. It may produce inaccurate, biased, or unsafe outputs, and generated content should be reviewed and verified before use in high-stakes settings.
Users are responsible for evaluating outputs, applying appropriate safeguards, and complying with applicable laws, regulations, and platform policies.
This repository and MiniCPM model weights are released under the Apache-2.0 License.
Please cite our paper if you find our work valuable:
pip install "vllm>=0.21"vllm serve openbmb/MiniCPM5-1B --port 8000curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openbmb/MiniCPM5-1B", "messages": [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}], "max_tokens": 128, "temperature": 0.7 }'pip install "sglang[srt]>=0.5.12"python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000curl http://localhost:30000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openbmb/MiniCPM5-1B", "messages": [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}], "max_tokens": 128, "temperature": 0.7 }'pip install -U "transformers>=5.6" accelerate torchfrom transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "openbmb/MiniCPM5-1B"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="auto", device_map="auto",)
messages = [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}]inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, enable_thinking=False, return_tensors="pt",).to(model.device)
outputs = model.generate(inputs, max_new_tokens=128)print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000 \ --tool-call-parser minicpm5 # or: --tool-call-parser autopip install flag-gems==4.2.1rc0pip install triton==3.5.1import flag_gemsflag_gems.enable(record=True, once=True, path="/root/gems.txt")vllm serve ${model_path} \--trust-remote-code \--dtype bfloat16 \--enforce-eager \--port ${Port} \--served-model-name ${model_name} \--gpu-memory-utilization 0.85@article{minicpm4, title={Minicpm4: Ultra-efficient llms on end devices}, author={MiniCPM, Team}, journal={arXiv preprint arXiv:2506.07900}, year={2025}}