InternLM#

InternLM is a bilingual (Chinese-English) language model series from Shanghai AI Laboratory, with versions 1, 2, and 3 each improving on reasoning, instruction following, and long-context capabilities.

Task

Text Generation

Architecture

InternLMForCausalLM / InternLM2ForCausalLM / InternLM3ForCausalLM

Parameters

7B – 8B

HF Org

internlm

Available Models#

  • InternLM3-8B-Instruct (InternLM3)

  • InternLM2-7B, InternLM2-Chat-7B (InternLM2)

  • InternLM-7B, InternLM-Chat-7B (InternLM v1)

Architectures#

  • InternLMForCausalLM — InternLM v1

  • InternLM2ForCausalLM — InternLM2

  • InternLM3ForCausalLM — InternLM3

Example HF Models#

Model

HF ID

InternLM3 8B Instruct

internlm/internlm3-8b-instruct

InternLM2 7B

internlm/internlm2-7b

InternLM 7B

internlm/internlm-7b

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#