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README.md

@@ -1,199 +1,380 @@
 ---
+license: apache-2.0
+language:
+- en
+- zh
 library_name: transformers
-tags: []
+pipeline_tag: text-generation
+tags:
+- minicpm
+- minicpm5
+- llama
+- text-generation
+- long-context
+- tool-calling
+- on-device
+- edge-ai
+- heretic
+- uncensored
+- decensored
+- abliterated
+datasets:
+- openbmb/Ultra-FineWeb
+- openbmb/Ultra-FineWeb-L3
+- openbmb/UltraData-Math
+- openbmb/UltraData-SFT-2605
 ---
+# This is a decensored version of [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0
 
-# Model Card for Model ID
+## Abliteration parameters
 
-<!-- Provide a quick summary of what the model is/does. -->
+| Parameter | Value |
+| :-------- | :---: |
+| **direction_index** | 14.35 |
+| **attn.o_proj.max_weight** | 0.97 |
+| **attn.o_proj.max_weight_position** | 18.94 |
+| **attn.o_proj.min_weight** | 0.89 |
+| **attn.o_proj.min_weight_distance** | 13.44 |
+| **mlp.down_proj.max_weight** | 1.28 |
+| **mlp.down_proj.max_weight_position** | 14.36 |
+| **mlp.down_proj.min_weight** | 1.20 |
+| **mlp.down_proj.min_weight_distance** | 12.32 |
 
+## Performance
 
+| Metric | This model | Original model ([openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)) |
+| :----- | :--------: | :---------------------------: |
+| **KL divergence** | 0.0001 | 0 *(by definition)* |
+| **Refusals** | 5/100 | 96/100 |
 
-## Model Details
+-----
 
-### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+<div align="center">
+<img src="https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm_logo.png" width="500em" />
+</div>
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+<p align="center">
+<a href="https://arxiv.org/pdf/2506.07900" target="_blank">MiniCPM Tech Report</a> |
+<a href="https://github.com/OpenBMB/MiniCPM" target="_blank">GitHub Repo</a> |
+<a href="https://ultradata.openbmb.cn/" target="_blank">UltraData</a> |
+<a href="https://github.com/OpenBMB/MiniCPM-Desk-Pet" target="_blank">MiniCPM Desk Pet</a> |
+<a href="https://huggingface.co/spaces/openbmb/MiniCPM5-1B-Demo" target="_blank">Online Demo</a>
+</p>
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+<p align="center">
+English |
+<a href="https://huggingface.co/openbmb/MiniCPM5-1B/blob/main/README-cn.md" target="_blank">中文</a>
+</p>
 
-### Model Sources [optional]
+## Highlights
 
-<!-- Provide the basic links for the model. -->
+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.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+🏆 **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.
 
-## Uses
+![MiniCPM5-1B capability comparison by domain](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/public_leaderboard_radar_en.png)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+🧠 **Hybrid Reasoning**: built-in `<think>` chat template, switch via `enable_thinking`. The same checkpoint serves as both a fast assistant and a deliberate reasoner.
 
-### Direct Use
+🛠️ **Deployment / Fine-tuning Resources**: the MiniCPM GitHub repo provides single-page cookbooks and Agent Skills for major inference backends and fine-tuning frameworks.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+🐱 **Desktop Pet**: a local-LLM desktop pet driven by MiniCPM5-1B.
 
-[More Information Needed]
+## Model List
 
-### Downstream Use [optional]
+Use this directory to choose the model format that matches your runtime:
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+- **[MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B) · BF16 final release (post-trained with RL + OPD) **👈 you are here**
+- **[MiniCPM5-1B-SFT](https://huggingface.co/openbmb/MiniCPM5-1B-SFT)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-SFT) · BF16 SFT-only checkpoint (before RL / OPD)
+- **[MiniCPM5-1B-Base](https://huggingface.co/openbmb/MiniCPM5-1B-Base)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-Base) · BF16 base checkpoint (pre-training only)
+- **[MiniCPM5-1B-GGUF](https://huggingface.co/openbmb/MiniCPM5-1B-GGUF)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-GGUF) · GGUF for llama.cpp / Ollama / LM Studio
+- **[MiniCPM5-1B-MLX](https://huggingface.co/openbmb/MiniCPM5-1B-MLX)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-MLX) · MLX / 4bit for Apple Silicon
 
-[More Information Needed]
+## Model Information
 
-### Out-of-Scope Use
+MiniCPM5-1B has the following features:
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- **Type**: Causal Language Model
+- **Architecture**: Standard `LlamaForCausalLM`
+- **Number of Parameters**: 1,080,632,832
+- **Number of Non-Embedding Parameters**: 679,552,512
+- **Number of Layers**: 24
+- **Number of Attention Heads (GQA)**: 16 for Q and 2 for KV
+- **Context Length**: 131,072
 
-[More Information Needed]
+## Introduction
 
-## Bias, Risks, and Limitations
+MiniCPM5-1B 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.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## Evaluation Results
 
-[More Information Needed]
+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.
 
-### Recommendations
+![MiniCPM-5 1B Public Leaderboard](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/public_leaderboard_en.png)
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## Training Recipe
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+The training of MiniCPM5-1B is a full-stack practice of **[UltraData Tiered Data Management](https://arxiv.org/pdf/2602.09003)**, covering three stages: base training, mid-training, and post-training.
 
-## How to Get Started with the Model
+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](https://huggingface.co/datasets/openbmb/Ultra-FineWeb), [Ultra-FineWeb-L3](https://huggingface.co/datasets/openbmb/Ultra-FineWeb-L3), and [UltraData-Math](https://huggingface.co/datasets/openbmb/UltraData-Math).
 
-Use the code below to get started with the model.
+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](https://huggingface.co/datasets/openbmb/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.
 
-[More Information Needed]
+![MiniCPM5-1B Training Recipe](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/training_recipe.png)
 
-## Training Details
+### What does RL + OPD bring?
 
-### Training Data
+**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.
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+**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](https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k) (inspired by [JustRL](https://arxiv.org/pdf/2512.16649)'s minimalist recipe) and uses a two-stage length schedule to reduce overlong responses while improving reasoning accuracy. We also use [TriviaQA](https://huggingface.co/datasets/mandarjoshi/trivia_qa), [NQ-Open](https://huggingface.co/datasets/google-research-datasets/nq_open), [LongWriter-Zero-RLData](https://huggingface.co/datasets/THU-KEG/LongWriter-Zero-RLData), synthesized verifiable RLVR data, and pair-wise RLHF signals to improve reliability, instruction following, and user experience.
 
-[More Information Needed]
+![MiniCPM5-1B RL Two-stage Pipeline](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/rl_two_stage_overview.png)
 
-### Training Procedure
+**OPD** builds on Thinking Machines Lab's [On-Policy Distillation](https://thinkingmachines.ai/blog/on-policy-distillation/) and incorporates implementation improvements from [Rethinking On-Policy Distillation](https://arxiv.org/pdf/2604.13016). 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.
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+![MiniCPM5-1B RL + OPD Gains](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/rl_gains.png)
 
-#### Preprocessing [optional]
+![MiniCPM5-1B RL + OPD Overlong Response Rate Drop](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/rl_overlong.png)
 
-[More Information Needed]
+## Quickstart
 
+### vLLM
 
-#### Training Hyperparameters
+```bash
+pip install "vllm>=0.21"
+vllm serve openbmb/MiniCPM5-1B --port 8000
+```
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+```bash
+curl 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
+  }'
+```
 
-#### Speeds, Sizes, Times [optional]
+### SGLang
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+```bash
+pip install "sglang[srt]>=0.5.12"
+python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000
+```
 
-[More Information Needed]
+```bash
+curl 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
+  }'
+```
 
-## Evaluation
+### Transformers
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+```bash
+pip install -U "transformers>=5.6" accelerate torch
+```
 
-### Testing Data, Factors & Metrics
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-#### Testing Data
+model_id = "openbmb/MiniCPM5-1B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
 
-<!-- This should link to a Dataset Card if possible. -->
+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_dict=True,
+    return_tensors="pt",
+).to(model.device)
 
-[More Information Needed]
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
 
-#### Factors
+Recommended chat template sampling:
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+| Mode | Recommended 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` |
 
-[More Information Needed]
+## Tool Calling
 
-#### Metrics
+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:
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+```bash
+python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000 \
+    --tool-call-parser minicpm5      # or: --tool-call-parser auto
+```
 
-[More Information Needed]
+## GitHub Cookbooks and Agent Skills
 
-### Results
+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.
 
-[More Information Needed]
+### Deployment
 
-#### Summary
+| Backend | Model format / use case | Cookbook | Agent Skill |
+| --- | --- | --- | --- |
+| Transformers | BF16 / FP16 local Python inference, GPU + CPU | [transformers.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/transformers.md) | [minicpm5-deploy-transformers](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-transformers/SKILL.md) |
+| vLLM | BF16 / FP16 OpenAI server | [vllm.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/vllm.md) | [minicpm5-deploy-vllm](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-vllm/SKILL.md) |
+| SGLang | BF16 / FP16 OpenAI server, recommended for tool calling | [sglang.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/sglang.md) | [minicpm5-deploy-sglang](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-sglang/SKILL.md) |
+| llama.cpp | GGUF local inference, CPU/GPU | [llama_cpp.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/llama_cpp.md) | [minicpm5-deploy-llama-cpp](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-llama-cpp/SKILL.md) |
+| Ollama | GGUF local on-device runtime | [ollama.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/ollama.md) | [minicpm5-deploy-ollama](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-ollama/SKILL.md) |
+| LM Studio | GGUF Mac desktop app and OpenAI server | [lmstudio.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/lmstudio.md) | [minicpm5-deploy-lmstudio](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-lmstudio/SKILL.md) |
+| MLX | MLX / 4bit local inference on Apple Silicon | [mlx.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/mlx.md) | [minicpm5-deploy-mlx](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-mlx/SKILL.md) |
+| ArcLight | GGUF local on-device, CPU, Desktop & Server | [arclight.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/arclight.md) | [minicpm5-deploy-arclight](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-arclight/SKILL.md) |
 
+### Fine-tuning
 
+| Framework | Use case | Cookbook | Agent Skill |
+| --- | --- | --- | --- |
+| TRL + PEFT | LoRA / SFT fine-tuning | [trl.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/trl.md) | [minicpm5-finetune-trl](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-trl/SKILL.md) |
+| LLaMA-Factory | Fine-tuning | [llamafactory.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/llamafactory.md) | [minicpm5-finetune-llamafactory](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-llamafactory/SKILL.md) |
+| ms-swift | Fine-tuning | [ms_swift.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/ms_swift.md) | [minicpm5-finetune-ms-swift](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-ms-swift/SKILL.md) |
+| unsloth | Fine-tuning | [unsloth.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/unsloth.md) | [minicpm5-finetune-unsloth](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-unsloth/SKILL.md) |
+| xtuner | Fine-tuning | [xtuner.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/xtuner.md) | [minicpm5-finetune-xtuner](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-xtuner/SKILL.md) |
 
-## Model Examination [optional]
+### Other Supported Frameworks
 
-<!-- Relevant interpretability work for the model goes here -->
+In addition to the deployment and fine-tuning frameworks listed above, MiniCPM5-1B is also supported by FlagOS for multi-chip deployment.
 
-[More Information Needed]
+#### FlagOS Overview
 
-## Environmental Impact
+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.
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+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.
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+Official website express: [https://flagos.io](https://flagos.io/)
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+<details>
+<summary>FlagOS multi-chip support and usage</summary>
 
-## Technical Specifications [optional]
+#### FlagOS: Supporting Multiple AI Chips
 
-### Model Architecture and Objective
+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:
 
-[More Information Needed]
+|Vendor|ModelScope|Huggingface|
+|---|---|---|
+|Nvidia|[MiniCPM5-1B-nvidia-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|[MiniCPM5-1B-nvidia-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|
+|Hygon|[MiniCPM5-1B-hygon-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|[MiniCPM5-1B-hygon-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|
+|Metax|[MiniCPM5-1B-metax-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-metax-FlagOS)|[MiniCPM5-1B-metax-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-metax-FlagOS)|
+|Iluvatar|[MiniCPM5-1B-iluvatar-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|[MiniCPM5-1B-iluvatar-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|
+|Zhenwu|[MiniCPM5-1B-zhenwu-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|[MiniCPM5-1B-zhenwu-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|
+|Mthreads|[MiniCPM5-1B-mthreads-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|[MiniCPM5-1B-mthreads-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|
+|Kunlunxin|[MiniCPM5-1B-kunlunxin-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|[MiniCPM5-1B-kunlunxin-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|
+|Ascend|[MiniCPM5-1B-ascend-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|[MiniCPM5-1B-ascend-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|
+|ARM-v9|[MiniCPM5-1B-Armv9-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|[MiniCPM5-1B-Armv9-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|
 
-### Compute Infrastructure
+#### FlagOS Usage
 
-[More Information Needed]
+##### FlagOS Performance Acceleration on Nvidia
 
-#### Hardware
+###### From FlagRelease (**Recommendation**)
 
-[More Information Needed]
+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.
 
-#### Software
+###### FlagRelease Image Key Versions
 
-[More Information Needed]
+###### FlagRelease Quick Start
 
-## Citation [optional]
+|Vendor|ModelScope|Huggingface|
+|---|---|---|
+|Nvidia|[MiniCPM5-1B-nvidia-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|[MiniCPM5-1B-nvidia-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|
+|Hygon|[MiniCPM5-1B-hygon-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|[MiniCPM5-1B-hygon-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|
+|Metax|[MiniCPM5-1B-metax-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-metax-FlagOS)|[MiniCPM5-1B-metax-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-metax-FlagOS)|
+|Iluvatar|[MiniCPM5-1B-iluvatar-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|[MiniCPM5-1B-iluvatar-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|
+|Zhenwu|[MiniCPM5-1B-zhenwu-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|[MiniCPM5-1B-zhenwu-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|
+|Mthreads|[MiniCPM5-1B-mthreads-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|[MiniCPM5-1B-mthreads-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|
+|Kunlunxin|[MiniCPM5-1B-kunlunxin-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|[MiniCPM5-1B-kunlunxin-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|
+|Ascend|[MiniCPM5-1B-ascend-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|[MiniCPM5-1B-ascend-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|
+|ARM-v9|[MiniCPM5-1B-Armv9-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|[MiniCPM5-1B-Armv9-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+###### From Scratch
 
-**BibTeX:**
+- Dependencies: Python 3.12, GLIBC 2.39, GLIBCXX 3.4.33, CXXABI 1.3.15
 
-[More Information Needed]
+###### Vllm Version
 
-**APA:**
+###### Installing the FlagOS Operator Library
 
-[More Information Needed]
+Official Repository: https://github.com/flagos-ai/FlagGems
 
-## Glossary [optional]
+```PowerShell
+pip install flag-gems==4.2.1rc0
+pip install triton==3.5.1
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+###### Activating Acceleration
 
-[More Information Needed]
+You can enable flagGems acceleration by adding the import of flagGems in the source code of vllm where inference is performed.
 
-## More Information [optional]
+```Bash
+import flag_gems
+flag_gems.enable(record=True, once=True, path="/root/gems.txt")
+```
 
-[More Information Needed]
+```PowerShell
+vllm serve ${model_path} \
+--trust-remote-code \
+--dtype bfloat16 \
+--enforce-eager \
+--port ${Port} \
+--served-model-name ${model_name} \
+--gpu-memory-utilization 0.85
+```
 
-## Model Card Authors [optional]
+##### Using FlagOS Unified Multi-Chip Backend Plugin
 
-[More Information Needed]
+[**vllm-plugin-FL**](https://github.com/flagos-ai/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.
 
-## Model Card Contact
+###### Using vllm-plugin-FL
 
-[More Information Needed]
+|Vendor|From Scratch|From FlagRelease||
+|---|---|---|---|
+|Nvidia|[vllm-plugin-FL/MiniCPM5-1B](https://github.com/flagos-ai/vllm-plugin-FL/blob/main/examples/minicpm/README.md)|[MiniCPM5-1B-ModelScope](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|[MiniCPM5-1B-nvidia-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|
+
+</details>
+
+## Desktop Pet
+
+We also ship **[OpenBMB/MiniCPM-Desk-Pet](https://github.com/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.
+
+<a href="https://youtu.be/Ee0slMW8SEk"><img src="https://img.youtube.com/vi/Ee0slMW8SEk/0.jpg" alt="MiniCPM Desk Pet video demo" width="720"></a>
+
+## Limitations and Responsible Use
+
+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.
+
+## License
+
+This repository and MiniCPM model weights are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
+
+## Citation
+
+Please cite our paper if you find our work valuable:
+
+```bibtex
+@article{minicpm4,
+  title={Minicpm4: Ultra-efficient llms on end devices},
+  author={MiniCPM, Team},
+  journal={arXiv preprint arXiv:2506.07900},
+  year={2025}
+}
+```