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# InternLM

[InternLM](https://github.com/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](https://huggingface.co/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`](https://huggingface.co/internlm/internlm3-8b-instruct) |
| InternLM2 7B          | [`internlm/internlm2-7b`](https://huggingface.co/internlm/internlm2-7b)                   |
| InternLM 7B           | [`internlm/internlm-7b`](https://huggingface.co/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](/get-started/installation)):

```bash
pip install nemo-automodel
```

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

```bash
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:

```bash
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:

```bash
docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.06.00
```

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

```bash
cd /opt/Automodel
```

**3. Fine-tune**:

```bash
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](/get-started/installation) and [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft).

## Fine-Tuning

See the [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft).

## Hugging Face Model Cards

* [internlm/internlm3-8b-instruct](https://huggingface.co/internlm/internlm3-8b-instruct)
* [internlm/internlm2-7b](https://huggingface.co/internlm/internlm2-7b)