Model CoverageLarge Language Models (LLMs)

MiniCPM

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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.

TaskText Generation
ArchitectureMiniCPMForCausalLM / MiniCPM3ForCausalLM
Parameters2B – 4B
HF Orgopenbmb

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

ModelHF ID
MiniCPM 2B SFTopenbmb/MiniCPM-2B-sft-bf16
MiniCPM3 4Bopenbmb/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.

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:26.04.00

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.

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