ChatGLM#

ChatGLM is a bilingual (Chinese-English) conversational language model from Tsinghua University (THUDM). ChatGLM2 and ChatGLM3 extend the original with improved performance, longer context, and more efficient attention.

Task

Text Generation

Architecture

ChatGLMModel

Parameters

6B

HF Org

THUDM

Available Models#

  • ChatGLM3-6B

  • ChatGLM2-6B

Architecture#

  • ChatGLMModel / ChatGLMForConditionalGeneration

Example HF Models#

Model

HF ID

ChatGLM3 6B

THUDM/chatglm3-6b

ChatGLM2 6B

THUDM/chatglm2-6b

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#