MiniCPM#

MiniCPM is a compact language model series from OpenBMB / Tsinghua University, designed to deliver strong performance at small parameter counts using model merging and continuous training techniques.

Task

Text Generation

Architecture

MiniCPMForCausalLM / MiniCPM3ForCausalLM

Parameters

2B – 4B

HF Org

openbmb

Available Models#

  • MiniCPM3-4B (MiniCPM3ForCausalLM): 4B

  • MiniCPM-2B-sft-bf16 (MiniCPMForCausalLM): 2B, SFT

  • MiniCPM-2B-dpo-bf16 (MiniCPMForCausalLM): 2B, DPO

Architectures#

  • MiniCPMForCausalLM — MiniCPM v1/v2

  • MiniCPM3ForCausalLM — MiniCPM3

Example HF Models#

Model

HF ID

MiniCPM 2B SFT

openbmb/MiniCPM-2B-sft-bf16

MiniCPM3 4B

openbmb/MiniCPM3-4B

Try with NeMo AutoModel#

Install NeMo AutoModel and follow the fine-tuning guide to configure a recipe for this model.

1. Install (full instructions):

pip install nemo-automodel

2. Clone the repo to get example recipes you can adapt:

git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel

3. Fine-tune by adapting a base LLM recipe — override the model ID on the CLI:

automodel --nproc-per-node=8 examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml \
  --model.pretrained_model_name_or_path <MODEL_HF_ID>

Replace <MODEL_HF_ID> with the model ID from Example HF Models above.

Run with Docker

1. Pull the container and mount a checkpoint directory:

docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.02.00

2. The recipes are at /opt/Automodel/examples/ — navigate there:

cd /opt/Automodel

3. Fine-tune:

automodel --nproc-per-node=8 examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml \
  --model.pretrained_model_name_or_path <MODEL_HF_ID>

See the Installation Guide and LLM Fine-Tuning Guide.

Fine-Tuning#

See the LLM Fine-Tuning Guide.

Hugging Face Model Cards#