Seed (ByteDance)

Seed-Coder and Seed-OSS are open-weight models from ByteDance. Both use the Qwen2ForCausalLM architecture under the hood.


Task	Text Generation
Architecture	`Qwen2ForCausalLM`
Parameters	8B – 36B
HF Org	ByteDance-Seed

Available Models

Seed-Coder-8B-Instruct: 8B code model
Seed-OSS-36B-Instruct: 36B general model

Architecture

Qwen2ForCausalLM (reuses Qwen2 architecture)

Example HF Models

Model	HF ID
Seed-Coder 8B Instruct	`ByteDance-Seed/Seed-Coder-8B-Instruct`
Seed-OSS 36B Instruct	`ByteDance-Seed/Seed-OSS-36B-Instruct`

Example Recipes

Recipe	Description
seed_coder_8b_instruct_squad.yaml	SFT — Seed-Coder 8B on SQuAD
seed_coder_8b_instruct_squad_peft.yaml	LoRA — Seed-Coder 8B on SQuAD
seed_oss_36B_hellaswag.yaml	SFT — Seed-OSS 36B on HellaSwag
seed_oss_36B_hellaswag_peft.yaml	LoRA — Seed-OSS 36B on HellaSwag

Try with NeMo AutoModel

1. Install (full instructions):

$ pip install nemo-automodel

2. Clone the repo to get the example recipes:

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

3. Run the recipe from inside the repo:

$ automodel --nproc-per-node=8 examples/llm_finetune/seed/seed_coder_8b_instruct_squad.yaml

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:26.06.00

2. Navigate to the AutoModel directory (where the recipes are):

$ cd /opt/Automodel

3. Run the recipe:

$ automodel --nproc-per-node=8 examples/llm_finetune/seed/seed_coder_8b_instruct_squad.yaml

See the Installation Guide and LLM Fine-Tuning Guide.

Fine-Tuning

See the LLM Fine-Tuning Guide.