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:26.02.00

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.

Hugging Face Model Cards#