Feat: add seedoss (#3104) [skip ci]

* feat: add seedoss cce

* feat: add seedoss config and docs

* fix: shouldn't have target modules with target linear

* feat: add vram numbers

* fix: hf link

* fix: name

* fix: support multipack seedoss

* fix: merge error

* feat: update seedoss instructions for transformers release
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NanoCode012
2025-09-10 09:01:02 +07:00
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# Finetune ByteDance's Seed-OSS with Axolotl
[Seed-OSS](https://huggingface.co/collections/ByteDance-Seed/seed-oss-68a609f4201e788db05b5dcd) are a series of 36B parameter open source models trained by ByteDance's Seed Team.
This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
## Getting started
1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Seed-OSS is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
Here is an example of how to install from main for pip:
```bash
# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
git clone https://github.com/axolotl-ai-cloud/axolotl.git
cd axolotl
pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
pip3 install --no-build-isolation -e '.[flash-attn]'
# Install Cut Cross Entropy
python scripts/cutcrossentropy_install.py | sh
```
2. Run the finetuning example:
```bash
axolotl train examples/seed-oss/seed-oss-36b-qlora.yaml
```
This config uses about 27.7 GiB VRAM.
Let us know how it goes. Happy finetuning! 🚀
### TIPS
- For inference, the official Seed Team recommends `top_p=0.95` and `temperature=1.1`.
- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
## Optimization Guides
- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
## Related Resources
- [ByteDance Seed Website](https://seed.bytedance.com/)
- [Axolotl Docs](https://docs.axolotl.ai)
- [Axolotl Website](https://axolotl.ai)
- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

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base_model: ByteDance-Seed/Seed-OSS-36B-Instruct
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
load_in_8bit: false
load_in_4bit: true
datasets:
- path: fozziethebeat/alpaca_messages_2k_test
type: chat_template
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/lora-out
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
bf16: auto
tf32: false
gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
# save_first_step: true # uncomment this to validate checkpoint saving works with your config

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@@ -34,6 +34,7 @@ plugins:
- arcee
- cohere
- cohere2
- deepseek_v3
- gemma
- gemma2
- gemma3
@@ -42,6 +43,7 @@ plugins:
- gemma3n_text
- glm
- glm4
- glm4_moe
- gpt_oss
- granite
- granitemoe
@@ -64,6 +66,7 @@ plugins:
- qwen3
- qwen3_moe
- smollm3
- seed_oss
- voxtral
## Citation

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@@ -38,6 +38,7 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
"smollm3",
"gpt_oss",
"arcee",
"seed_oss",
]