Model CoverageLarge Language Models (LLMs)

Seed (ByteDance)

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

TaskText Generation
ArchitectureQwen2ForCausalLM
Parameters8B – 36B
HF OrgByteDance-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

ModelHF ID
Seed-Coder 8B InstructByteDance-Seed/Seed-Coder-8B-Instruct
Seed-OSS 36B InstructByteDance-Seed/Seed-OSS-36B-Instruct

Example Recipes

RecipeDescription
seed_coder_8b_instruct_squad.yamlSFT — Seed-Coder 8B on SQuAD
seed_coder_8b_instruct_squad_peft.yamlLoRA — Seed-Coder 8B on SQuAD
seed_oss_36B_hellaswag.yamlSFT — Seed-OSS 36B on HellaSwag
seed_oss_36B_hellaswag_peft.yamlLoRA — 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

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:26.04.00

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.

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