koshuro/MiniCPM5-1B-heretic

This is a decensored version of openbmb/MiniCPM5-1B, made using Heretic v1.4.0

This model is reproducible!

See the README in the reproduce directory for more information.

Abliteration parameters

Parameter	Value
direction_index	12.35
attn.o_proj.max_weight	1.30
attn.o_proj.max_weight_position	16.19
attn.o_proj.min_weight	1.13
attn.o_proj.min_weight_distance	13.02
mlp.down_proj.max_weight	1.46
mlp.down_proj.max_weight_position	15.35
mlp.down_proj.min_weight	0.60
mlp.down_proj.min_weight_distance	8.95

Performance

Metric	This model	Original model (openbmb/MiniCPM5-1B)
KL divergence	0.0383	0 (by definition)
Refusals	3/100	95/100

Available Model files:

• minicpm5-1b-heretic-q4_k_m.gguf
• minicpm5-1b-heretic-q5_k_m.gguf
• minicpm5-1b-heretic-q8_0.gguf

MiniCPM Tech Report | GitHub Repo | UltraData | MiniCPM Desk Pet | Online Demo

English | 中文

Highlights

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.

Model List

Use this directory to choose the model format that matches your runtime:

• MiniCPM5-1B · ModelScope · BF16 final release (post-trained with RL + OPD) 👈 you are here
• MiniCPM5-1B-SFT · ModelScope · BF16 SFT-only checkpoint (before RL / OPD)
• MiniCPM5-1B-Base · ModelScope · BF16 base checkpoint (pre-training only)
• MiniCPM5-1B-GGUF · ModelScope · GGUF for llama.cpp / Ollama / LM Studio
• MiniCPM5-1B-MLX · ModelScope · MLX / 4bit for Apple Silicon

Model Information

MiniCPM5-1B has the following features:

• 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

Introduction

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.

Evaluation Results

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.

Training Recipe

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.

What does RL + OPD bring?

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 (inspired by JustRL's minimalist recipe) and uses 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.

Tool Calling

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:

python -m sglang.launch_server --model-path koshuro/MiniCPM5-1B-heretic --port 30000 \
    --tool-call-parser minicpm5      # or: --tool-call-parser auto

GitHub Cookbooks and Agent Skills

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.

Deployment

Backend	Model format / use case	Cookbook	Agent Skill
Transformers	BF16 / FP16 local Python inference, GPU + CPU	transformers.md	minicpm5-deploy-transformers
vLLM	BF16 / FP16 OpenAI server	vllm.md	minicpm5-deploy-vllm
SGLang	BF16 / FP16 OpenAI server, recommended for tool calling	sglang.md	minicpm5-deploy-sglang
llama.cpp	GGUF local inference, CPU/GPU	llama_cpp.md	minicpm5-deploy-llama-cpp
Ollama	GGUF local on-device runtime	ollama.md	minicpm5-deploy-ollama
LM Studio	GGUF Mac desktop app and OpenAI server	lmstudio.md	minicpm5-deploy-lmstudio
MLX	MLX / 4bit local inference on Apple Silicon	mlx.md	minicpm5-deploy-mlx
ArcLight	GGUF local on-device, CPU, Desktop & Server	arclight.md	minicpm5-deploy-arclight

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 License.

Citation

Please cite our paper if you find our work valuable:

@article{minicpm4,
  title={Minicpm4: Ultra-efficient llms on end devices},
  author={MiniCPM, Team},
  journal={arXiv preprint arXiv:2506.07900},
  year={2025}
}